{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GoRL 目标导向强化学习\n",
    "\n",
    "* HER(hindsight experience replay)算法，事后经验回放\n",
    "\n",
    "* 仅适用于异策略，因为主要改进在经验采样部分"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何理解HER算法\n",
    "\n",
    "接下来看看如何实现 HER 算法。首先定义一个简单二维平面上的环境。在一个二维网格世界上，每个维度的位置范围是[0,5]，在每一个序列的初始，智能体都处于[0,0]的位置，环境将自动从3.5<=x, y<=4.5的矩形区域内生成一个目标。每个时刻智能体可以选择纵向和横向分别移动[-1,1]作为这一时刻的动作。当智能体距离目标足够近的时候，它将收到奖励并结束任务，否则奖励为-1。每一条序列的最大长度为 50。\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "个人理解是，经验池改成储存轨迹，在轨迹上采样，并且有比较大的概率把负奖励改成0，或者把0改成正奖励，从而提高经验利用效率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "class WorldEnv:\n",
    "    def __init__(self):\n",
    "        self.distance_threshold = 0.15\n",
    "        self.action_bound = 1\n",
    "\n",
    "    def reset(self):  # 重置环境\n",
    "        # 初始状态随机生成一个真目标状态, 从[3.5 ~ 4.5, 3.5 ~ 4.5]中随机取\n",
    "        self.goal = np.array([4 + random.uniform(-0.5, 0.5), 4 + random.uniform(-0.5, 0.5)])\n",
    "        self.state = np.array([0, 0])  # 初始状态\n",
    "        self.count = 0\n",
    "        return np.hstack((self.state, self.goal))\n",
    "\n",
    "    def step(self, action:list):\n",
    "        '''\n",
    "        动作包含两个值，x、y两个方向移动量，限制在-1~1\n",
    "        '''\n",
    "        action = np.clip(action, -self.action_bound, self.action_bound)\n",
    "        x = max(0, min(5, self.state[0] + action[0]))  # 新状态限制在0~5\n",
    "        y = max(0, min(5, self.state[1] + action[1]))\n",
    "        self.state = np.array([x, y])\n",
    "        self.count += 1\n",
    "\n",
    "        dis = np.sqrt(np.sum(np.square(self.state - self.goal)))  # 和真目标的距离\n",
    "        reward = -1.0 if dis > self.distance_threshold else 0\n",
    "        if dis <= self.distance_threshold or self.count == 50:  # 最多50步\n",
    "            done = True\n",
    "        else:\n",
    "            done = False\n",
    "\n",
    "        return np.hstack((self.state, self.goal)), reward, done  # * 注意state设计，同时给了 “当前位置” 和 “目标位置”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolicyNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, action_bound):\n",
    "        super(PolicyNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.fc3 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "        self.action_bound = action_bound  # action_bound是环境可以接受的动作最大值\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc2(F.relu(self.fc1(x))))\n",
    "        return torch.tanh(self.fc3(x)) * self.action_bound\n",
    "\n",
    "\n",
    "class QValueNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(QValueNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim + action_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.fc3 = torch.nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x, a):\n",
    "        cat = torch.cat([x, a], dim=1)  # 拼接状态和动作\n",
    "        x = F.relu(self.fc2(F.relu(self.fc1(cat))))\n",
    "        return self.fc3(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DDPG\n",
    "\n",
    "在定义好 Actor 和 Critic 的网络结构之后，来看一下 DDPG 算法的代码。之前的 DDPG 算法是在倒立摆环境中运行的，动作只有 1 维，而这里的环境中动作有 2 维，导致选择动作函数用的是detach分离action出计算图，而不是item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DDPG:\n",
    "    ''' DDPG算法 '''\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, action_bound,\n",
    "                 actor_lr, critic_lr, sigma, tau, gamma, device):\n",
    "        self.action_dim = action_dim\n",
    "        self.actor = PolicyNet(state_dim, hidden_dim, action_dim, action_bound).to(device)\n",
    "        self.critic = QValueNet(state_dim, hidden_dim, action_dim).to(device)\n",
    "        self.target_actor = PolicyNet(state_dim, hidden_dim, action_dim, action_bound).to(device)\n",
    "        self.target_critic = QValueNet(state_dim, hidden_dim, action_dim).to(device)\n",
    "        # 初始化目标价值网络并使其参数和价值网络一样\n",
    "        self.target_critic.load_state_dict(self.critic.state_dict())\n",
    "        # 初始化目标策略网络并使其参数和策略网络一样\n",
    "        self.target_actor.load_state_dict(self.actor.state_dict())\n",
    "        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)\n",
    "        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)\n",
    "        self.gamma = gamma\n",
    "        self.sigma = sigma  # 高斯噪声的标准差,均值直接设为0\n",
    "        self.tau = tau  # 目标网络软更新参数\n",
    "        self.action_bound = action_bound\n",
    "        self.device = device\n",
    "\n",
    "    def take_action(self, state):\n",
    "        state = torch.tensor(state[np.newaxis, :], dtype=torch.float).to(self.device)\n",
    "        action = self.actor(state).detach().cpu().numpy()[0]  # * 动作是数组, 用.detach()把action分离出计算图\n",
    "        # 给动作添加噪声，增加探索\n",
    "        action = action + self.sigma * np.random.randn(self.action_dim)\n",
    "        return action\n",
    "\n",
    "    def soft_update(self, net, target_net):\n",
    "        for param_target, param in zip(target_net.parameters(), net.parameters()):\n",
    "            param_target.data.copy_(param_target.data * (1.0 - self.tau) + param.data * self.tau)\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        states = torch.tensor(transition_dict['states'], dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions'], dtype=torch.float).to(self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'], dtype=torch.int).view(-1, 1).to(self.device)\n",
    "\n",
    "        next_q_values = self.target_critic(next_states, self.target_actor(next_states))\n",
    "        q_targets = rewards + self.gamma * next_q_values * (1 - dones)\n",
    "        # MSE损失函数\n",
    "        critic_loss = torch.mean(F.mse_loss(self.critic(states, actions), q_targets))\n",
    "        self.critic_optimizer.zero_grad()\n",
    "        critic_loss.backward()\n",
    "        self.critic_optimizer.step()\n",
    "\n",
    "        # 策略网络就是为了使Q值最大化\n",
    "        actor_loss = -torch.mean(self.critic(states, self.actor(states)))\n",
    "        self.actor_optimizer.zero_grad()\n",
    "        actor_loss.backward()\n",
    "        self.actor_optimizer.step()\n",
    "\n",
    "        self.soft_update(self.actor, self.target_actor)  # 软更新策略网络\n",
    "        self.soft_update(self.critic, self.target_critic)  # 软更新价值网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 轨迹经验池\n",
    "\n",
    "接下来定义一个特殊的经验回放池，此时回放池内不再存储每一步的数据，而是存储一整条轨迹。这是 HER 算法中的核心部分，之后可以用 HER 算法从该经验回放池中构建新的数据来帮助策略训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trajectory:\n",
    "    ''' 用来记录一条完整轨迹 '''\n",
    "    def __init__(self, init_state):\n",
    "        self.states = [init_state]\n",
    "        self.actions = []\n",
    "        self.rewards = []\n",
    "        self.dones = []\n",
    "        self.length = 0\n",
    "\n",
    "    def store_step(self, action, state, reward, done):\n",
    "        self.actions.append(action)\n",
    "        self.states.append(state)\n",
    "        self.rewards.append(reward)\n",
    "        self.dones.append(done)\n",
    "        self.length += 1\n",
    "\n",
    "\n",
    "class ReplayBuffer_Trajectory:\n",
    "    ''' 存储轨迹的经验回放池 '''\n",
    "    def __init__(self, capacity):\n",
    "        self.buffer = collections.deque(maxlen=capacity)\n",
    "\n",
    "    def add_trajectory(self, trajectory):\n",
    "        self.buffer.append(trajectory)  # 一次存一整个轨迹\n",
    "\n",
    "    def size(self):\n",
    "        return len(self.buffer)\n",
    "\n",
    "    def sample(self, batch_size, use_her, dis_threshold=0.15, her_ratio=0.8):\n",
    "        batch = dict(states=[],\n",
    "                     actions=[],\n",
    "                     next_states=[],\n",
    "                     rewards=[],\n",
    "                     dones=[])\n",
    "        for _ in range(batch_size):\n",
    "            traj = random.sample(self.buffer, 1)[0]  # 随机从self.buffer中抽1个轨迹并取出\n",
    "            step_state = np.random.randint(traj.length)  # 从轨迹中取某个时点\n",
    "            state = traj.states[step_state]\n",
    "            next_state = traj.states[step_state + 1]\n",
    "            action = traj.actions[step_state]\n",
    "            reward = traj.rewards[step_state]\n",
    "            done = traj.dones[step_state]\n",
    "            \n",
    "            # 如果使用HER，在0.8的概率下执行\n",
    "            if use_her and np.random.uniform() <= her_ratio:\n",
    "                # 从step_state + 1, traj.length + 1中均匀取一个数, 作为新的目标的索引, 注意与环境内的真实目标区分\n",
    "                step_goal = np.random.randint(step_state + 1, traj.length + 1)\n",
    "                # ↓使用HER算法的future方案, 在当前轨迹随机取一个点, 设置为目标状态, 状态前两个值是坐标值\n",
    "                goal = traj.states[step_goal][:2]\n",
    "                dis = np.sqrt(np.sum(np.square(next_state[:2] - goal)))  # 计算下一个状态位置和目标值的距离\n",
    "                reward = -1.0 if dis > dis_threshold else 0  # 如果下一状态离目标比较近, 不惩罚\n",
    "                done = False if dis > dis_threshold else True  # 如果下一状态离目标比较近, 结束\n",
    "                state = np.hstack((state[:2], goal))  # 更新state中的goal\n",
    "                next_state = np.hstack((next_state[:2], goal))  # 更新next_state中的goal\n",
    "                \n",
    "            batch['states'].append(state)\n",
    "            batch['next_states'].append(next_state)\n",
    "            batch['actions'].append(action)\n",
    "            batch['rewards'].append(reward)\n",
    "            batch['dones'].append(done)\n",
    "\n",
    "        batch['states'] = np.array(batch['states'])\n",
    "        batch['next_states'] = np.array(batch['next_states'])\n",
    "        batch['actions'] = np.array(batch['actions'])\n",
    "        return batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                   \r"
     ]
    }
   ],
   "source": [
    "actor_lr = 1e-3\n",
    "critic_lr = 1e-3\n",
    "hidden_dim = 128\n",
    "state_dim = 4\n",
    "action_dim = 2\n",
    "action_bound = 1\n",
    "sigma = 0.1\n",
    "tau = 0.005\n",
    "gamma = 0.98\n",
    "num_episodes = 200\n",
    "iteration = 3\n",
    "n_train = 20\n",
    "batch_size = 256\n",
    "minimal_episodes = 200\n",
    "buffer_size = 10000  # 经验长度\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "env = WorldEnv()\n",
    "replay_buffer = ReplayBuffer_Trajectory(buffer_size)\n",
    "agent = DDPG(state_dim, hidden_dim, action_dim, action_bound, actor_lr,\n",
    "             critic_lr, sigma, tau, gamma, device)\n",
    "\n",
    "return_list = []\n",
    "for i in range(iteration):\n",
    "    with tqdm(total=int(num_episodes), desc=f'{i+1}/{iteration}', leave=False) as pbar:\n",
    "        for i_episode in range(int(num_episodes)):\n",
    "            episode_return = 0\n",
    "            state = env.reset()\n",
    "            traj = Trajectory(state)\n",
    "            done = False\n",
    "            while not done:\n",
    "                action = agent.take_action(state)\n",
    "                state, reward, done = env.step(action)\n",
    "                episode_return += reward\n",
    "                traj.store_step(action, state, reward, done)  # 记录轨迹\n",
    "            replay_buffer.add_trajectory(traj)  # 保存一整条轨迹\n",
    "            return_list.append(episode_return)  # 记录episode奖励\n",
    "            if replay_buffer.size() >= minimal_episodes:\n",
    "                for _ in range(n_train):  # 更新n次(20次)\n",
    "                    transition_dict = replay_buffer.sample(batch_size, True)  # 改成False为普通采样，效果很差\n",
    "                    agent.update(transition_dict)\n",
    "            if (i_episode + 1) % 10 == 0:\n",
    "                pbar.set_postfix({\n",
    "                    'episode': '%d' % (num_episodes * i + i_episode + 1),\n",
    "                    'return': '%.3f' % np.mean(return_list[-10:])\n",
    "                })\n",
    "            pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCCHEjwIjQgghhBA/CowIIYQQQvwoMCKEEEII8aPAiBBCCCHEjwIjQgghhBA/muAxTqIoQhDaZ+onlmXa7dw9DdWVdlRX8aH60o7qSjuqK+3ao65Ylok5234ABUZxEgQRtbUtCT+vTsciLc2CxkY7vF4h4efvSaiutKO6ig/Vl3ZUV9pRXWnXXnWVnm4Bx2kLjKgpjRBCCCHEjwIjQgghhBA/CowIIYQQQvwoMCKEEEII8aPAiBBCCCHEjwIjQgghhBA/CowIIYQQQvwoMCKEEEII8aPAiBBCCCHEjwIjQgghhBA/CowIIYQQQvwoMCKEEEII8aPAiBBCCCHET9fZBSCEdA1OtxcHy5vQv68VZqMO9c0uVNY5MDg3BSyrbVXqozUtcHsEDMi2Rt3P4+Wx70gjcrOSkWzWh22vrHeg2e5BYY4tbJuXF7DvSCOy05NQ1+SCQc+iX4ZF2l7T4ERdkwuDcm1gGPVy1zY6UdPoRF5WMg5VNGFAthUmg/LrsK7Jhap6BwbnpYCNcB4AOFLdAi8voH9f5XsWRRF7jzQi1WJAZqoZANBod+PA0SZYk/QY2M+Gilo77C4vBvaz+etFwO7SegDAkLxUuDw8yqqawTAMdBwLj5dHYY4Neh0HADhQ3giTQYfs9CQcLG+CQc9Cz7Gob3YjJzMJ+440wmjgMDg3JWJdAMDhymbUNDqh4xgMyknB4cpmZKWa0eL0ACKQ1ycZpVXNEEUgv0+yoh53HqxDYT8bWJbxveeyRnAcA7eHx+C8FHCs+t/fjS1uHK1pwaDcFOi48H32HWlEslmHPmlJite9vIC9ZQ3Q6ziIEFHYz4bqBieOVLegb3oSstN9+7c4PThc0Qwdx4JhAa/X9zM6XNmMvmlmpCQbI9aHw+XF3iMNMOg41Z+/IIrYU9qAzBQT0m0mON1eHDjq+xyZjb7PUaTPRSyNdjeOVgfrJfQzEi95fTXZ3eiTZlb8vgDAoYom6DgWRj0X83cHCL7/rFQz0qy+epR+X/zfF6IoYk9ZAzJsvjpSE/i+SLUaUR74LOg6P19DgREhBACw+fdK1Db6bjBTx+fhy5/LAAAsw2BwXoqmc2z6rQIAkJFiUg14An7ZV4tDFU3Yebge554wMGz7hl+OAgBsFj0yU8yKbTsP1WN3aT1+O1ArvTZzSqH0eP22IwAAs5FDblYy1Hy91bfPr/t956ioc+DEMTmKfb7a4nv/Oo5VDdAAX/Cz+ffge7aYgu+5scWN7ftqYE0y4JRj8qSyl1U1AwD6pJnx3a/lAIB0qxEpyUaUVjVj1+F6f/l1+GVfDQRBVFzT7vJi3JAsNDs82LK7GgAw7Zg8/Ly7SrHfvqONsDs9AIDMlOANLJTHy+OnXVUQRd91DpY3weMVFPtkpZnxw45KAEDfNDMMel9g9vF3B+BwuAEAg3NTcKS6Bdv310jHCSIwfECa6nW/+LkMoiiCF0SMKEhXbKtpcGLbXt97k/9sAeC3A3XYd6RBem5NMuDHnZVwuXnsP9qI80oGgmEYbPil3BfYyfx+sM5/jvDzyu090oA9pb5rpFmNyEpVfgZLK5ulz9/MKYX4eXc1KmrtOFLTgmnj8xSfi8wUE2xRgrBQX/1cBl4Q4fLwGD0oU/qMpCYbI/4Mo9l9uB47/Z8pAIo6AgC704ufdik/O9F+dwDgwNEmxfsHgr8vBj2Lgmwbahqc+HV/LVKTjTh5XG7YOQRRlL4v5EYWZsT3BttB54dmhJAuobbRCQBoaHErXm9occV9Lpebj7q9xn+t0Jt+KLvTG/ZaXZO28jQ7PLF38gu8dzX1zZGvJy996PUCwYXLE6wLryzgaJTVs93le5/y92t3eVXrp7LOAQBwy85b1xheRrssKPB4I/887C5eCork5VaeK1gup8rPNvAzCa2DqnpHxOsGrlndEF738oCGF5TlqQn5WTU2uxWft0D5Q4OieMjfr7yepfKFvk//zyTwM5VVJ5pVPsPR8P6feXmtQ/FzabK7Ix0SVbXKZ9vtCdap0x1evpYYZY72+9Li8CrOoVZ/gPp3RJO99T+zRKLAiBDSI4nRY66oBI0HywMX+c1Gfg4vL0g3OFEWStXLArzAzdzhkgUgrug3J3kZG2PcNPkoAWis6wDKIMPhv5GKKnUU2vyiFmRpIX9vaoGYXGgwFmt/LeTn8PLh75OTNf3xggCOC29qC3C3sjwuD6/4uamVo7UcsmDIpRK4hL6fUF5e/bMO+DJGQDDg8vDqnwG1n1MgE9nZKDAihPRIbbmNyP+aVQsAgttkx4TcYAI3NUEQpRuHfP86WSYqcJOQ3yzsMQIW+Y2ysaUNgZH/mnqVvh1GTwOMngZlxsjl21/+fiPVkTtCpir0xhrK4Qoe53RFDyyaHMr37lDJgMRLHqCqlVXe587p5qEP6SMlrw+1wEMLnhcUgWW0rF+85J8zh0r9cjH6FIYGO/Lfl8DnyOF/jedF1c+HWqbKqO8aIUnXKAUhhCRahJt1aNOMGkXmJspf/EKUG6A8FvF6VQKjJnlg5Lue/KYeKdgJnEN+w47W3Af4bk6RBN5rakg/GIO3GZP2PYNJ+5+Do7kxrKzygCXw3kP763o8sbMFakGb/KYZK9AJrSeni291pgrw/UxdioxR+LnkTaIOl1fRYVgQRcg/Yq0NjABl3bQ2E6b2s1d+vsPrN1KH+QB5UC6IoqJsgc9n4LyCvx9ZKLX3E63Dd0eizteEkB4pUigQCFKicci+tKMGRvIbV8hf3qJsm4cXYASnaErzKG6uvn4+TpW+MnI63gm31wxRFBU37IiBgCiCE9xRmwYD10y1GlFVZ0d6y14U1HyDNPshaR/9kW2AYYSvrP795QFL4L2H3tgiXdfpjt5kKK9Lp8MJvj4YmLG8ExzvBs8aAIaBxytA723B8KPvweKugUc8EU7bmdL+nOCGABYiq7zdCaKoOtrQ5eYV5VZrCvIK8j46vCKDFNp3Jt6AhuNY8P5ryn+uTjcPUfACXg8YgznS4WHkPyeOd8LA22H4bROav/wJXL+hcPY5I+yYWPGJ/LPH84Ii0AoEQfKfoZcXwkYeOlR+7rH6HHYUCowIId2Cy8Ojut6huf+P2l6iKOJwZXPEY1qcHjRXNClu1naXF/uPNiIvy4KqeidcHh55WRYwDIPSquC5Dlc2YVRhOmoanXC6lf1DahudqKi1ozlC59KGFjcOVjRJN8RQjMijuGwVspp2YH/miXB7rowa4KU374XVWY5+DVtg8Lagoe9CQJoSgEdpVQt4QUSGzSTdOK2sG8cefBE2R1nY+ZKrtgG5vsCortGJ0qpmlFW1+LY5jgItXhwsT8WR6pawY/cfbUSyWY+GFjc4lkFOpgUHjjZJ210eHnanB+W1wZ9toM8UIwqwff0o7Paj0v7j/f9WWwahNnkQIIrIrf8JFrd/NNyOd9FSux/pxmLYHGUYUPsdnPoUbBp4reKOv+tQPfR6FilJBri9gpT5CARtZnctMpt3w9vAoiV5CpIy++FojR2pyQZF3e870qjIypRVt6BfRnCKgbomF3aX1sPW4EJjkwN6lkVOlgUuN4+aRic4lkGL0wujnoOOYxSfgcDUDYDvM1K76gHoGo+AOWke0G8kKv2d2w06FgY9B7vTC2uSHo0tbtgsBjQ7PFKgZvA247h9T0PP+44RAXibqpBZ04RDfc5T/MxKq5rB8yJMRg5Ndg90HAOzUSd1jna5vOjT+BuaTNk4VJGhCIjLqprBsQyaZH2/dh1uQE5mEiwmPY7UtEAU1TvdH61pQZJZj+Jk9eH9HYUCI0JIt7BhezkaYjQZyan1awgdTh5q7aZDMJsN4JjgsTwvYOueapTX2lFRawfg++vd4fLiUEWT4vgfd1ahos63j7yfRuhw6FB2Z3DofaCPhjxbkFv3E/o07QAAFFZ/DeeBsfAaBwbeqOKGb3UcxdjDr0D+R3/yb+9CHDoUDMtiV2kDdvuHb+t1rDTvjvW3VdDJgiKXLhnVliHIbfgZBmewzhpa3PhhRyVYloHZpMO4AyugEzzYxMxFs6lf2Hvbuqda8fxoTYs0si7gx11VqFG5UWY3bIPZfhQCWPCcUbqpA0Bmy15ktuwNllefgjpzPrIbt8NSuRXjsDX4/l2VsLiq0GLq42/rEbHjYG14asT/mUm1H8SY0tehE3wBmmv1F2iccj02V1rAsYxiHqDQEVrb99UgMyV4Y2+yu/HL3hqYzQY4HG4IgojjdNnYtrdGMXJQjTzQ9DQ3wFC3HwAgfPEk9vWfCUNjKZx6Gw6nHxc9zSOKGFS5TlF/AlgwEJBWsxXH2GtRkzwYBzJOABgW5TV2lNfYI54us2knRpW9BRFAefVoHM2fgb4Ne5Desg+7+p6OrSEjR/cdacC+I755j9RGKqa17EN2wy/Yn3kittjd0Bl06J+ZFLZfR6HAiBDSLcQTFAFQTRlpHurv/8vYoOdgMelQ1+RCtewL3eXmw4IiAKhuCO4TrcNzNHqOhU7HwuMN9p3Jbtim3OmnNyGMnY9kZznGHVoJZ3oR9huHocnUD4Oq1oEB0GTsC7cuCRkt+2Gq2QnXpjdgmjxbMdTc4xXg8bqR5KoGd+gHAMChcTdgt9M395DFWYHchp+lG6rNYoDbI0hZFZ3XAZ3gO9+II+9hZ/YZaEjqL53fYtZL10u1H8So0v/hUPpxMNtGwqWzItV+EIwowIl8AGZkWA1Idx8B57XDoOOQVrceAFBTeAbq8k9CZW0zssu/weCqdQCAFkMm+NR8iAYL+KGn4tcjAiqtw1FQvR42Vzk8nFkq+8Dqr1CeMhrDj74PA2+HU2fDgcwS2BxlSLUfQp1lIHLrf1JUc5MxG4AIq6sC3PplyM+aiqMpY+DhozdlyZvP8vskg+VYJFuMOHikHs12D9zVpXA3uwGd9pt/oTE4bxcr8hh8cJX0fFDVOhzMOAEN5ny4dUkYceQ9OAxp+CX3IuRyNRj6+7/B+H9Ou/rOgN7bgsPpx2FI5Sfo17ANqY7DSHUchtldi8Ppx6HZ1Fe1DDpGQKGuEunNPwIAGAD9Grahn+zz2WzKxuH0ScGyCm6k2g/BYUhHk70PACAr1QyTgZOyt0MqPoXVVYGchq3gGQ4p+fOAzOA5OhoFRoSQHkOeJVILS4yG+IYD9++TjDSbCd//XqEIdCI157U2GJJjWQYmA4dGf7Igu2EbUpxlEMFg79iF6P/LczA0HkWfH5+CibHCwDtgqNqKMbIMiQAWv+RdBIchHX0bfkHxkXfg2f4pDCOmqo5CsjrLwUAE23cwRh57DCp+KkVjixtezhcA6HgHIIoYWZCOgxVNOFLtC4z6JQffr9VVgQkHX8KW/NlwcxbfDTe/EJvFIQDDoKh8LQy8A4OrvsDgqi8gMhwY0VcWuz4Nmwrnobj8Y+gObVaUjUlKxcCTz0OhzoDvtgs46C3BoYzJsDnK4EkrwPSJBQDgy9QdKUeVbTiqrMOQaj+IvoOGgt/5NQYcWYu+Tb+jb9Pv0nlN3kYMK/9Qep5UX6e4rqffGPxgOxOAiDGH30C6fT+KKtYis2knDqVfo/qz69P4K1LthyCkTwaQCpNBh2OG9oFOxyItzYKWFhe46n1I/fIlTDBkYFPhtRAZ9c/kiIJ0aRLFvFQWeZv+63ufKcXwMgbk1P8kZQU5kUdh9dfKn6mrAsVlq5CJWikoEsdfhMOO4dI+O/ueDoO3CRktvkxUTsNW5DRsxaaB16DZlB1WpsKGTcgv+1R6fjD9OGQ17UKSJxi05ddugslTj9LUCejT9Dv6126EgbfDyxrw7eC/AJwJQ/unIjPFjPSmPagt3Q+rKzjRIysKMOkEJG4MXvwoMCKE9BjyeEUtdjHGO08KA5hDgylRBNyRmxkCWMGD4rK30GjOw4HMKZovyXEszLLlSQZVfg4AqM+eCCY9Dz8N+COOLXsVxpZyZKFc2s+rM0Pn9WVHjqSOh8Pgm026ImUUCp2/Ial2Jzx7NsHJj/OVj2GkAM8g+pqD2KRUAJA6ynr8gRELXyduMFB0ojUI4c0iYw+/FnxStxknsUZ4ODPMnnrFfozIQwQLntUhyVOHkj1LfAEYw4HN7A+AAcNyMIw7B4zOAAAwGX0/C5Hh0JDUH0myoEInH0nFMKi3FKC/xYpDGeOQ3LAbGS37AADltmLs6XMKBlV9och0AIDXnAFTv0EwTJiJajEVwnZf/W7Nn41RNR8hs/pnpNsPoNReA8ACk0EnZc8MniYUl70NBiLEb39Aev5sODOHQxRFiC47AAtSj27CoINvAgAs7mqU7H4cO7LPRosxE17OBLcuONt0klEHHe8EzxqQu+stQPBdp9lWiAPJY7G7z2kwJpkxPM+G2o3vIqN5D2zOo2BkfxL0bfrNXx8sLBc/CN6SBXx3UNrOcyZs6X8FGFHAqNI3kNW8GwCQ2bQLzaZscIIbBm8zHPo0gGGQ1uyrQ8bWF2WmwdiTPg17+pwGneCEjndi8t7lMHvq0b92E/rXblLUrU5wo2/DL7AbM2D48g00Ve5Gpigg07+92dgHPw64EhlpFpw7dhTq6sL7q3UUCowIIT2GMpMTHhlFW/MsktA11IaWf4i8HT+irmAOmsy+ZUT0OlbRJ8joacCQik+R1bwbWc27UZ08RPUvcDWcP2MEAIzghcnra7KrHTgDZgOHFmMfHB43Hzk/PweDqw68JQspsx/GL998iQE7VqLJnIMD2acozumw5iOpdicEez1cTHAUWqB/jBm+JkbG6Os7E5iXR2D14BkdONELveAAA2VgpOd9AWKDKQc7s8/CxAP/lra5OAsMggM6wQWd4Du/XZ+GA5lTwIgCcm1AqaUYKP8VI46+Dz3vgMjqkHTq9dAVjNP0s5BTW2PLbOQgGpOxpf8fYHUchcVdhXJbMcCw+C1nJpy6FAysWY9yWzHK0o7BoHHHIs2/Fpxe1ndIYHXYk38+WHsd0u0HkF75E6rSpsBk5OB0e8EKbowpfV0KShiIyK3/GQfTC+H44BHw5bvAjzoZabvWK8pn4O0YXfY/AL5P69b82ahJHoJkZzmsP3+Ekw5+r9hfP3QKmm3HAM08eM4Ik9EAc5IZ+7NOxv6sk2H0NCCjeQ+qrMOR3bgNOt6J/n2ssBYWg03JBhMh0ykyLHbkXQDDgf8ixXkEfZt+hcDqMLD6a+gEN+rM/XEw43gkN/j6dJlPux77douAfyoCL2eGlzNja/5spNoPYWBN8H0eyDgBZnct+jb9jmEVH0X8+VVah8PLmcHoO7fjNUCBESGkB1E0pSVg5C8DX5DCMIx07rx6X/+KgdVfYVv+bAC+GzYveCAIIkzuehx74HkY+GBWaUDNBvyaMyv2OGj4AyN/h2ij19cHg2c4iIZkKTBoZtPw++i/wF11CMNGDUcqy8KeOQLfDrkJbs6CFKsJLlmfLI/ed7PnWxogWHzD1FOTDVJgZPRnjAKBkXzmYy9nAudt9mdzGOhl2wL9dzxcEppM2Si3FcPobcS+zJNRnzQAJw61YMvWXb46MupRx2XAI/reQ3ZRFsSaFpSnjEWjORec4MaxxxZDl6ZcN03OEGWBUb3KbM0mgw6MvxN8k7kfmszKzuH7sk7GkdRxcOpTAIZRZAe50OHlbi+OpI5Huv0Asiu/w17bBJj0ZkAUUVTxCWxO38i5Cutw9G36HTZHGfof+RR8la/5rmnr52ABuLhkbB9xHYSmGhRUf4NU+0GwEMAJbow9/BoqrMOR1bQTLJQjFPXDTobpxKtg3FEJNPs+F2ajTvqsAL4O6EfSjgEAX4dsAP3H5ELnX2Mt0jxBLMPAZEnGr7nn4/i9TyLZVYUhlZ9J29Mch5BWGpy+gU3LhYc/FHae2mTfSEG3Lgn9Gn7BzuzT0WjOQ2bTDqkZ08saoM8aCM6aDmfNEWy1nQwva0Sz0devKdYcSh2BAiNCSI8h7+ITeGh3esEwvptItFgp0oSKLMvAqOfCJsLT88GMAsv4bsJ2pwfZDdsUQREAZDduR1rLflRZh2Fnv7Ok13W8A+MOrkRjUh52Zp8pXS+QMTJ6ffP3uHVWMBwrNSVV1NmRbNaj2ZwLncnXgZdjWakpxmrWKzqr13uMyAbgaakHLL6+VmbZDTUQGMHoz5bIggIPZ4bR2ww9788YyYITztMi7QOGwa+55yvety45HY1m3wK6bLIRBl6Ax98Z22TU+a7DMGgx+jrlGizRFysOnQsn1jaTgYueJWQYOA2pwf1ldRIaaAmCiArbSAyo+QZWVyVO2rUYwl4DRvLBz01j+gjszDgDfZt+h8nbCFOVrzlJP+hYGE0m1DR58LNuLFq8JsCci235lwAA8tM4FG58wNfcFOgHldIPh9j+EBkWLQNPxqRjhgJQNu2aDJz0WYlEbUbzUDod6+vXZkjHzr4zkGo/jMzmXeBELyr6z0Ba2XrpM83lFYNhuahzDpWmT0KprAN2dXIR9mdMQaM5B9XWoTjnhAJwLIuGWjvqfy1XHBtr1u2OQIERIaTbE0VRkdUJvObxCvjke99ftueVDIy6vMe6n0oVE/XJmQzhgZFOFhgxLAMTx8Lu9MDoDR+tBgBGvgV59T/6AiD/zbqg+hvYXOWwucqlwIhjGSloCZzLpbP6Xpc1JQXWCAsEBPIAwJqkV1y7Bb7mCXdTPdDHFyTKzxXI/DAmf1Oa7GbqYc3+fZxhTWk6r+9m6eHUR1fpZMGFjmOh17HSKDWzgQvLyuh00W+KgcAwINkcfA9qgZHRwEW80VqTDLC7vIp5g0xRMkYAAIbBvqypGFP6BgCAlQVFNWmjUF18BTxVzXCZMmD0T3GgKzgGyTNuQFqaBUd+L0fL7+Eryhstydg8cC6O37s8+OKMRdj9i69Tc35ysO+RPHgzG3RRg0VA+TOIuI/ssxUIavReO0yeeqT0L8JOYzGS3DUozLYiv2gIACDJpI853YCEYbGvz1QAvs9p4LMaupQKEHudto5AgREhpMeQxz2CIIYtMBo3/3e0ycgBIfNC6mUdj1kmPJhx6FNRaR2G+qT+KKj+BinOI9J2L2uETnAi09/ZFfD1JxJZHTiWgc3MoiBDD4vTF3y59DawDANrkl7R4RcIjrSTJ0by+iSjocUNHcfiUEUT3Jwv4DF4fRmewbkpyEo1ITsjCTwvwlyt7GOU39eKZqcHdqdXGpmm5x1hna9ZtyxjpEK+L8cyKMyxgRdE2CwGJJv1ihsjyzIx+4D1STVjQLYVHq8AUQRGFWYojpcbUZAOlmEU9WIx6WGzGCBCRPHADHi8AnYeroMo+pZEkZfXqOcwJC9VMcki4Mt+HEqfhBRHKfSFx+IH92CIDIO09DRY/Mcfzp0BW+VPqB1wCiZMnhCsD5XszcB+NmSmmLHHkIEDGcejoGYDapMKkGWy4PhiA/YfbcKIgmDzYm6mBVX+eaByMn0/r9GDMlHf7EJ+n2TsPdKAhma3NLN0aOB04pgc7CqtR5/UJGzbWy2Vq6CfDQdlU1B4dEnw6JKQwbEYPXIgymv7IndIJlj/+SaN6IvfD9YCIuD2CuBYRpqjyGLWI8ViwODcFOwqrUdtowtuf38knY6VmvTSrEbkZSXDwwvSHGGR/jjpSBQYEUISIlo2pqPIO18LYvg6V4HNfdOSpIkYtVDr9KvIGDHBbIPB3y9oV98ZqLb6mj+qrcNw3N7lsLhrkF+7Cbl1P0AnKoM2A2+Hi7WBYxg41jyMQbWlEJJ8N0SnzgqO8fURmTSiL77aEpyIMXBdef2bjTpMHN4XFbV2X2Ck891AdYILOpGXbqjHjfB1CG/53QEBwcAoxWLAcSOy8dOuKino0fN2MGAUGQjWEyMwCgkE+qYnoW96MLskP5eeY2OulcUwDMYNyYq6D+ALaoryU33HyKa6PHlcDvQ6ZdYpUAdqRg5MR3mtHU12WTMrw2B33xlIMukxqjAdnt98GSCTUSdlvKqsw3DQMBiZyWbFewptnhtVmIFBuSnS/Ff7Mk+GU5+GSuswTGMY9ElLQp80ZTbObNRhcrGyzIU5NulxVqoZX289IgVGoRmzdJsJx43IVjQd6zgWaVYjBuemYE9Zg2J/Hccgv08y8vskK14PfEYCDlc2S4FRflYyhg3wzYV13IhsbPytXJowUhcSDE8Y1gdHa1qkwIj6GBFCSAKI8CV3QjNGkVZxZ/xBhtZgLmzIPgBODGZtWCbYFBHoMB0IRgLshnRY3DUYUPud6jX0vB0unRUm+xEIFXt8523wZZmc+hRY/Tc4eXOPQc9JNxL5WwlkXgJ/fXtZEwSwYCFAL4QPgxZdvjIzRuXNj2UZ6X0UVn0J8asKcJwBNv14NFvywdjrIcKXXVATKwOkyCjFaBJKhNZkI+RzU8nXMTMZOEX5TUYddP7zu/wjFEPv8aHZm0CzWOB1kdWhzN95OlHrqUYKNuWfo0CAqrYvo7HO5LuF1rN8KgW1TvLy/amPESGEJJAiYySIilXAfQtB+J8zvi/jaKvO+3cDEGWYuH85DqnDtChKGSOXzqrY1W7IAOBrOiu3jURZ2gSkN+9Fv4ZtMHkbMfbQq+A5A3R635w94AwA70aLIQMVtlFI8d8w5JNUygMPtUknpZsM4wtwTN4mpLYcAjBW9hZEiC5fsBTIGMnPX5Y6Hv0atsLobQZT7psXZzzzKw5mlUCs8s1r02jKVa+fGBRD/zugb0lrpmuQB9e+tcx8j81GZf8e38/f9zgQPIVeL7QjdCDgVusn1JqyxkOvY6VAL3D9tlxSEdyE/CyV21T6FTEUGBFCeqDOb0gLyRiJ4RkjUYqLGLAMNM+uG9rpN8DAt8CtS/Y1pRk56HiHNMw60K8noCZ5CPLqfsDRlLHYmX06wLCoTxqAVMdhmLyNMPLNvgL5WzjMM26Ewy3g+0M68JxJurlEumFGDYwA1CUVoF/jLxhS/iE8B/IgNlZCP/IUiI5GQPDVRGhgxLEMnIY0bBp4LdJb9mFYfgp0hzaDO/obCiu/AADwRptiZFc85E1tsToRJ0Kspjo1Xtn8VL7snK+ufBmj4PnMBp1iLiu164VljPwBt1on5NaUNR4M4wvmWxyCdP22BGPRsj7y52rvlTJGhBDSTuRDiN0ePmpTGsey8EB9e6hAMxkjKvfPq92MfX2mgWUYGBneF9wA4HVJEFnl12udZSC+GroobAmI0ADKnTYQtmPOgC6vGKzTA77sMAD1m5YI+Si88HLL/zrf0e8sWJ3lSHZXwfnJUgCAt+w3MAb/cP/sIjB6o+L4wD3Ko7OgImUUhhXmQlc0Cfs+eEma2djZd1T4hTWSZ4nUOiZ3BfKAU37TDh0RZjJwYU2zYU1KYYGRP2OkMhqvneMi6fotDo9U923KGCmyPsr3Ka83tZ9ztGxTZ6DAiBDS/fk7GclvTA0tbjTsr1Xso+yHE/u0waY03w2MFZVD9gfWfAOPLgnJQjrwzZuY6H+dT1KfpFBtXSz5UPfapAJ4T/gLMnJS/GWUdXRWuWuZZEucxPpLW2AN2NP3VMWSHfzh4JIYhokXhh2j1idHbzBid98ZOJR1Ik7PqUKVbghQGntxXrUbomLofwIzRlrm7tGKZRkp4JbXscnAKa5j0HPwhATiodWn6LjOMlGzgG3J3mh9/4GAP9A3Si1LpdOYwZHXTejnRhEYqZxP8TnvAhmjrhmiE0JIK2gdGMcwjOZOpYDvRpOXlYwUc/hXZr/6reiz731A5MH6F0UV+w7VfG63rONyi7GPoqMqG+Fmc+zwvrAmGTCuKDhCq3hgOmwWg2LUlsWkQ1/ZqKaa5CH4vmgBki58AIaxZ0uvm06eC112UVjZQm9SjH+4fl5WMjL7ZiFr8tkoGJiD1GSjYrQZAEwe6RuxNG5IFmwWA0YWpIWd32YxIN1mgsmgk0bKtcVEf71MGNanzecKmDwyG9YkA0pG9VPUh8nISXXRL8MCi0mHdKsy4xYaaDAMgwHZVuh1LIbkpiheL+hnC9m39WUeVZgBm8WA8UXRR/Dl9UmGNckg/ezkAYrRwCE12agY8RaN/L2GBj/yejOoDGRgFYFT54cllDEihCRGF+hkFGuUmSitZRVfXwaG8Q0rdtQx8G7xrV6/YfANKNnzhGJlcEn2cKBa27l9nbJ9Gsx50ugzIHLWIDfTgtyQQCLJpMe08Xlh5Z5cnI3fDtRi1+F6AIDTlAUuPRfs+HMAlgPXbyh0uSNUrxN6/cCzCcP6SPPRGHQcTh6Xi/pmlzTkesroHGSk+CaVHJBtxYBsZUf0AB3H4sQxOarbWiMn05KQAEsuK9WMU47x1Svnr0Mg2D9IHoTpdRxGD8qU5gdS+/mNG5KlOuXA2MGZOFTRJGWn2tLHKNkc/llQk52ehGxZQMvIYpKh+WmagyIgRsYotJN6iK6WMaLAiBDS7Yn+trRoPYZEiIrgTVNTRcg+rH+Fc4HVwaVPQYshAxZ3Dbz6ZNjOuQXOb/4L0dUCtm8RUK0tMqq0jcQW1pdpqEkejP6KG4ymU8R+GyqPGZ0Rxgmzoh4XdpOKUmfRbow9hbwPW6SlOJTNZfGdv7NrTf47EW9cprXztVllhKe8nqjzNSGEJJAYZf0mIBgXMUzrbt6Mv4+RwPi+OvdmTUO/hq1oGTETozILkHTenQAYCHHMuC0yLGqswWYseakSNmRbfp44ThkpY6RGERh1/r2tXbi98qH76lGP/PX2HlmWaPLixvvZk//MowVGMTNGXaDOKDAihCSE2AXa0tSGrCvINrfm6zc0MKqyDUeVbTjyzb6mIsbfFqE2JLk1EnVjVWaMtJ8zPGMUeV/5SKR4+m91J6HD8dXIO5nHfZNnGHRmm7T8sxHvHw7R5iqS14N88WLVa3WBj07n93IihJAEiR0XSTkjTV/AobuwvDIwiiTWCKuO/qu4tZcL63wdx749kdsbe+Yrxfpv3axK2DZk/aI1w3mFYEBpjNH5uitUGWWMCCHdXiAgitb5eu3mw0hL9vXlYZj4MifSdXhfE5nAhn65K68bc0XzTkwMxBMkhXcxitLHSP6eOz952C6EGE21gLIeuluwqGhxjTOalu8feqTXG6y3mFMTdIGmNMoYEUJ6jGj3LbeHlxaOZdDK71+NGSOGYZCRYoLFpMfQ/NSwpoUJQ/uAZRnF6vABfdKUC7JmZyTBqOcUo4fiFe2mFU08mS2WYZBuMyHZrEeyWR/HVdrXiIHpYFkGQ/2LyrbFscP7gvMvfBqJvg19jDo7JJCXN96gTscxSE02wppkkNaAC8jrY4FBz2FAX/XRiQCQmWJGkkmPNKshvkK3A8oYEUISQuscQu1bBl8hdBwbcdZrAL6WNJXv/dRkI1yy9dNC95EyRiGBkdp7LxnVDyJ8AcPQ/mn4YONBaQ2tzBQTzp5cAIYBftlXEzxmdL+wZrhJw/tCFBOYfYjjZh26a6xDp4wOvueuIsViwNmTCxJSf7mZFvRLj34u+c9P6yLFXYViIdi4u0cxOGlsjurP32TQ4fSJ/aPW2wmjsiEifNbszkCBESGkxwjchziOQbTuIIz/v1AsywDRFpaVAiP1odqKazDBK4RPlBic9ZhhGOkGqnZTYBimza0LrRyUFhYJxTpW/p67kkQ2acU6l7wZVUvTm1xnx5PKfkKtGLUZ5ecfq9660men80MzQghJkMCoNC7WlzoD6S6f0bwbReUfgRG8sb+YI2SM4tXqQKW110PrLhi+a1e5dXVd8oCC72YZo7YM1+9JKGNECOkxAoFRzL9OEbzFB9YOc+pT4UifFvW4YOfrkKY0DWWLWCJZR+x2uxe1MhAL3bcX3ytbJd6MUWdrSx+jnoQyRoSQHkNqSouZtkfYXd/kaYgdNUTsYxTfDVDRZCEvV1xn0U5xjbj6GPXem2Mi8N0uMFJ/3NtQYEQI6fZCh+vH/mtXvY9RrF4OosZRaTEpIxX1xwmkGJUWT1NanJ2viRIfrb+ais4ORLvaDNSdhQIjQkiPEQiQtDQDaFsqLXSmOheA8M7XoSujxzyvhseJlKg+TV2ne2zXlpnim3KhIMLiuV0VNaX5UB8jQkhCdIV+poE+HZqa0uJ4PcBb+isAoMXoWx09J9OC4oHpSDLFN29PxAxOh/TEbkNTWu+9V8bl+OJsON3euD8Xna0tw/V7EgqMCCE9gC8g0tqU5pvgUbmPGOGuL4oC+LLf4Nr0Pwg1hyCCQaVtBABfAJbIm1/XzxgRLdhWfi46u36VAXtnl6bzUGBECOkxAlM6xpwkjlG/CandCzw7voZr/UvS84bMMXDrkoMnaqOOuBm1drh++JIgiSkP6ZpouL4PBUaEkATp/LY0KWMU40udgfZJE4WGCulx0nl34tdqC1Dr8F9He9kiNt9pP0VCtGWCx87PafRwXah6u8AE1J2mF791QkhPE1jEO1YfI98NiAFEQfFaaOdihgEg+EaiGcaeBa7vYLDyO0YibmQdMES6tYuD0jxGvVdvbkqjwIgQ0u3FO1zf18cIYMUo64YECP59/JM6JrrpSx6MdblbUZcrUM/W2aP+5Dnf3tyURoERIaTHCA7Xj74fAyA7PUlbYOSfuwgsJx0rP09idcA8RnEcF3pz7MX3yg4xoK+v71pGiqlTrm82BKeh6M0/a+pjRAhJiK4wXD8g5l+7DIN+GUmYOCwD2OV/TURYp2wGDER/xojh2ilj1BFNaRGfxHueXny37ABD+6chPcWEdGvnBEZ6HYeTx+WCZZle3ZRGgREhpEuJd3kNINgEIPofxfpSDwzXz0zWocX/GosI2SMhkDHyfV2yCQ5kOuT2oxiUFs88Ru1QFhIRyzLom5bUqWVITTZ26vW7gm7flPbNN9/gpJNOQmlpadi2o0ePYsGCBRg/fjyOPfZY/P3vf0dzc3MnlJIQ0pE0jxYTgsEQE6lZLdCUppoxak3pQiT6fGqXUL9cXMepv0BIz9MtM0aiKGLdunVYuXIlvvvuO9V9nE4nrrrqKuh0Otxzzz2oqanBkiVL0NTUhKVLl3ZwiQnp+bpQS1rsjJF/s+hfFBbwdcRm5BsB/8C1kD5Grcy+RCyL4nEHDEuL67CQPkaJKAshXVy3DIyamppw/fXX46STTsKCBQvw5JNPhu2zatUqHD58GGvXrkV+fj4AwOv1YvHixThw4AAKCgo6uNSEkHYXb3QWyAYhygi1QB8j1VFpcV4vli6WMQo7D7WtkV6gWwZGZrMZn376KfLz8/H222+r7rNu3TqMGTNGCooA4Mwzz8TixYuxcePGNgVGOl3iWyA5jlX8SyKjutIunrqSD3HX6VjpOcuxmj7zvChKx3C66MdwLCPtG7qfIDuPTnaeaEPwdf4yspzvvBzHRt8/cF4mOI+R0duIrEOfwsLrsD/1WIBlwXEMGH9gxOn10nFS+TTWja/8LFhWDHvPgTIDgD5GvbWWvMxcHGXWC8HjGIZRHEe/h9pRXWnXFeqqWwZGer1eEfCo2b17N6ZOnap4LScnB2azGQcPHmz1tVmWQVqapdXHx2Kzmdvt3D0N1ZV2WurKbDZIj9PSLNLzZItR02fe6fZKx6SkmJEWpROpOckAf8NV2LkFQZTOY7OZpe3y8oVKTU2CyaiDxWKC2eyC1WqKur/V6juvo4lDU+Ac9sPAwcO+MjAcyvseD5vNDI4V4AWQbLPAkmaBzdqiWr5YzGY9dF4u7D0nmQ0Q/HWRnm6BXsepHt8WLR4h7p8nADhdwZ8pw6h/99HvoXZUV9p1Zl11y8BIi7q6OqSmpoa9brVa29QBWxBENDba21AydRzHwmYzo7HRAZ4XYh/Qi1FdaRdPXTkcbulxXV2L9Ly5xYW6upZIh0lcbl46pqHBAX2Udi2H3Q2HwyNdS04QRek8jY0O1Jl1YeULVV9vh9HAoaXFCYfDjeZmZ9T9m5udqKtrgade/bsg5+iX2G8dj6ZGJ8xuXzmbHV6461oU5w6cRwuHwwOPlw97z3ZHsC7q6+3QtcNfyo2NDqnMLXa95jK7PMGfKcswiuPo91A7qivt2quubDaz5ixUlwqMvF4vysrKIm5PTU1FSkqKpnPxPK/aHs4wbZ+fwettvw82zwvtev6ehOpKOy11JQjBQMbrFaTngsZ6lh/De6MfwwuitG/ofkLItsB2eflCebw8OJaB1+s7lueFqPsLvAivVwDvUQ+eDN4WsF4XeF6QOmgL4OD1ChBl5RMEUfNnUBAE1fcs8CF10Q692OXXEATtvzeCvB4Z9e8++j3UjupKu86sqy4VGJWXl2P69OkRty9cuBDXXXedpnNZLBbVzFBTU5NqJokQ0nPEHN0ljUqLPPM1K3p9+wX2kUalJWaEl/o+rTq1hovLH8ZzkeC+XWnUISHtqUsFRnl5edi5c2dCzlVQUID9+/crXquoqIDdbseQIUMScg1CSJAou3WKXfw2Kt3uZcP1w/bxj1ILDNcPjkoL7pPo9aTaa7h+q+cxokFopBfqsV3kS0pKsHnzZlRWVkqvrVmzBkajESUlJZ1YMkJI+9EYkAVu+II34i7S8P0oEzwmPI5pt4wRRTiEaNVjA6PLL78cSUlJmDdvHj788EO89NJLWLZsGebMmYO0tLTOLh4hJILWZJvCVhHRGAeIfHhg5OV8SyKwoteXwQmZ4JFtx3mMOqAlLa6mwN68wjrpvXpsYJSZmYkVK1bAaDRi0aJFWLFiBebNm4cFCxZ0dtEIIe1E6zJrUpNVSGDE68wQWH9gJASa0gJ9jMKb0rrLoqpMhMfxHNiaNewI6Y66VB+j1jj//PNx/vnnq24bMWIEXnvttQ4uESG9k+K+2cn30Fg3fyZCU5pXnywFQkxgUdnA8/ZaK01Rrg6Y+rr9DyOkW+uxGSNCSPfUkYmJ0KY0rzEFIuNvMhO8vsCHj7JWWjeJHOSZrfg6X3eTN0hIAlFgRAjpeWKN1o+QMXInZUH0N5mxIu+L0qQ+Rv7X5RmjOHIqnRliKOMbCnYIiYYCI0JIwnX13igMGPA1hyFUK5cH8pgzgxkj0QuIwQnmgk1psvN0kxhDnvmJsoQcIQQ9oI8RIYQEaA7IvC7YP3oA8DgVL7vNmTAHOlmLgjKjxKr1MeqGUUY3LDIhHYkyRoSQbi+0X5JaExcreGBy1wEAOEdtWFAEAB5TuiJjxAiymbE5lT5GbSx3R+mOI+kI6SyUMSKEJIQ8OOmsprRoQ8qPOfgf2JxHsLlgLlhHcAX7uowxMDSVgREFeEL6GDGiLGPEhC8J0l0SRtTFiBDtKDAihPQ4agGLzXkEANCvYQu4lEEAAC53JA7nX4zqejsABnksB4EJBEZeaVkQsJwUECmDjG4SZSg6jBNCoqHAiBDSpbRuuL72g/S8A6zD16TGpmQDDCs1nwGQMkaMyEtzGAX6FwGha6XFUcROjEhau1YaIb0R9TEihPQqOllgxCRnKLYxAMTA0h8iH+xjxAUDo265TAa1pRGiGQVGhJAEkXcy6roD9vW8A6y9FgDAJqeHbRflTWmBWa/ZYEZJmTHqHkEGZYwI0Y4CI0JIjxExHpNtMHkawNUdAACw6bnhuwYyRgIPiGpNad1vWBpDfYwI0YwCI0JIFxN/tin0iNCsCCsGh90b+RYwghdsag7YtLzwcwVGn4EHE5jHiFPvY0TzGBHS81BgRAhJiK6whqwY4cqs6Al7TTdkMhiGUQZRTLDzNSsEO18zrHofo3hiDI7tvK9bmseIEO0oMCKE9DihN39OUAZGosECw8hTVI8VWFkfI97lP4G8j1Hr5jE6dlgfJJn0mDCsj/aDEoT6XhOiHQ3XJ4R0Ka3ptx3rGFY2UeOh9EnIKj4ONkOS+rmY4ASPukbf3EdsSj9pe2uzL2lWI6Yfm695/8SiPkaEaEWBESGk54gQIAUyRm7Ogt19ZyCjT7a0jQkJGgKdr63Ocph/3+I7vk9hcJ/uOPN19+svTkinoaY0QkhCiF2hk1EEgT5GPKsHED04CGSMrK7y4PGKwAiyx90wzOiOZSakA1FgRAjpMSLFY6x/dFlguY+o52DD9+EyBwTPRRkjQno0CowIIV1KIpJNoQELF5IxQshINPmBoYGR5bJHweiMqufuLkGGormwuxSakE5CgREhJOE6rSUtwoVDM0bRm9KCI9C8Kflgw5YNkQcZ3STK6CbFJKQroMCIENIjjSoMBjThGaPIkYIom8xRsOWEbQ+d96g76CbFJKRLoMCIENK1tGq4fvhBg3JTcPbxBTDouagZo9CggdeZpceuAZPDztst10rrHsUkpEug4fqEkB4jdOZrHef72y+QMRJijEpjALTYCrAr+3Q0mHIwJHNw+D7dMsrojmUmpHNQYEQISQh51kYtg9ORQhdNZf3zGPGMSufrsIM5lGVMgiCIqrux3TDG6JaxHCGdhJrSCCE9RqR4LDDzdWC5j7asFyYPujo7ACSEJB4FRoSQLiXSQrDRj1EKDXs4KWPkD4widDJimNjZle6YfemOZSaks1BgRAhJiK6cO5EyRoGmNK1UAgpFxqgthepAbcmQEdLbUGBECOnZGN+CsAAgsFyMnTWdTpJk7CbdNCkuIkSzbvJbTQjpLeTddjR34YnRlsYEAiOpKU3eOVu5c6wYgmEYnDFpAARRlEa9dXUUFxGiHQVGhJAeI3Lna19gJJ/VWotIAYXR0PbMU0fqnlMMENI5usefO4SQrq81mZ52ouxbzcj6GPkCGooTCCGRUGBECOkxIo1oCzal+QMj+bbQICnqRkJIT0eBESGk2wsLh0ICmrCmtAgBDzU5EUIoMCKEJIQY4XFn61e1ERkt+wCoZ4xC0dB2Qno3CowIIT1HSEQmtNSh8MiHwedSxkjb6ShEIqT3ocCIENKlKIbra809hfT2DgQ0oseh3C1GxogCIUIIBUaEkB4jfDoj5VdccFRa5BCIuhkR0rtRYEQISYxWzczYPgLBjSgKitfV5jGiIIkQIkeBESGk5+I9iqdCrAkeKRAipNejwIgQ0sUEs01aE08RdxO8yqcaJnik2IiQ3o0CI0JIQnTF4foirwyM1DpfUyBECJGjwIgQ0mOIoSkmPjRjFFgeMlrKqGNDpcwUEwB0mwVpCenpaBFZQkiX0ppsU2g8JHWojrMpzTe5Y8fmu4oLM2Ax65GTaenQ6xJC1FFgRAjpMUJDmkhNaVp1xBIheh2LovzUdr8OIUQbyt0SQhJC7EKdjKRwJsKotKidr6nTESG9GgVGhJCeIzQgE3jlU6nzdaRFZNujUISQ7oQCI0JIl5KQeSIDEzyGZIxElbXSaOg+IUSO+hgRQsKEje6K9/gElUMrp9uL2kaVq4Z0vhY1/C1IwRAhvRsFRoSQbm/TbxXqG0I6XwfSQ705+DEa4uuAnmTSw+70wGSg2wXpHeiTTgjpssRW5p4CgY8YkjGStisiIybyxh4UQU0c3hdV9Q7072ON67jji7Ox63A9huSltFPJCOlaKDAihPRcoRkjSQ+KeDTKybS0aq6kZLMe44uy2qFEhHRN1PmaEBKmdZMsiqqPO0Ug6xMhMKLRZ4SQSCgwIoT0WJGa0uSYkBFqtI4aIb0bBUaEkC5FkW1qa+IpZLh+AGWMCCGRUGBECAnXyS1hbRWc+ZqPsJ0iI0KIOgqMCCE9liioZ4zkmJBnimwSpZYI6XVoVBohpEtJxJJrwYyRso+RQc8hL8sClqWAhxCijgIjQkjPJQuMmox9ML4oC9npSTEOYlQeEUJ6i27blLZ69Wqce+65GDVqFKZOnYr7778fjY2Nin2OHj2KBQsWYPz48Tj22GPx97//Hc3NzZ1UYkK6j9ZMrJiQNc4SJbBWmn9U2uG0ifix4M9R9w15SAjppbplxmjjxo144IEHcMUVV2DYsGHYtWsXnn/+eezfvx8vvPACAMDpdOKqq66CTqfDPffcg5qaGixZsgRNTU1YunRpJ78DQkiH8GeMGs054FlDJxeGENIddMvAKD09He+//z769u0LADjttNNgNpvx8MMPo6ysDLm5uVi1ahUOHz6MtWvXIj8/HwDg9XqxePFiHDhwAAUFBZ34DgghESWik1GAP2MkML6vOi0ZoR66IgghRKNu2ZRWVFQkBUUBhYWFAIDa2loAwLp16zBmzBgpKAKAM888E4Av40QIiaw1TWGtXdesPQSG44v+eYwEJvLCqTR0nxAi1y0zRmp+/PFHGAwGKRO0e/duTJ06VbFPTk4OzGYzDh482KZr6XSJjyc5jlX8SyKjutIunrqSj9TS6VjpOcuxmj7zOi54DMcxUY/hWEbaN3Q/+bXl54lnJBmn85WZEfzzGLE6sCwjva7Yl2Nk12MBng8+V9mf+NDvoXZUV9p1hbrqEYHRxo0b8dJLL2H27NmwWn0rR9fV1SE1NTVsX6vV2qYO2CzLIC0t/oUYtbLZzO127p6G6ko7LXVlNgf74KSlWaTnyRajps+8R2SkY6w2c9RjzEkG8P5MTdh+Oi54batJ2i4vXyypKUlISzOjGTx4AHqTEWazASkp4eVKTm6C2ewEACQlGWB3O2A06mXniTWKrXej30PtqK6068y66lKBkdfrRVlZWcTtqampSElJUbz2xhtv4L777sPUqVNx6623Sq/zPA9GZXI2hmFUX9dKEEQ0NtpbfXwkHMfCZjOjsdEBnhcSfv6ehOpKu3jqyuFwS4/r6lqk580tLtTVtcS8VkODQzqmqdGBujp95GvZ3XA4PNK1FOdpdgXP0+SUtsvLF7ssdugggPf4ruH0iHA43GhscMDMKX//m1uc0rntdt+/LpcHgiCiocEOPdN1mgi7Evo91I7qSrv2qiubzaw5C9WlAqPy8nJMnz494vaFCxfiuuuuAwC43W7cddddeP/993HDDTfg2muvVQQ8FotFNTPU1NSkmkmKh9fbfh9snhfa9fw9CdWVdlrqShCCAYDXK0jPBY31LD/GG+MYXhCD+4bs5+WD2+TllpcvlsBxotcXGHlFFoIgqpaLl11PEAQwjO9akfYnSvR7qB3VlXadWVddKjDKy8vDzp07Y+4nCAJuvPFGbN++HStXrsT48ePD9ikoKMD+/fsVr1VUVMBut2PIkCEJKzMhpAvzj0oT4xiVJkcdswnpfbplT7D33nsP33zzDf773/+qBkUAUFJSgs2bN6OyslJ6bc2aNTAajSgpKemoohJC4iUbEtfWiSJFPjBcP9qoNEIICepSGSOtvvjiC/Tt2xelpaUoLS1VbMvJycHgwYNx+eWX49VXX8W8efMwZ84cVFZWYtmyZZgzZw7S0tI6qeSEdA+tG67ftuMTSWpW1xAYqRyd+AIRQrqNbhkY1dXVobS0FHPnzg3bdskll+Dee+9FZmYmVqxYgfvuuw+LFi1Ceno65s2bh2uuuaYTSkwI6RQhEzwixsALX0Alyp63V8EIIV1VtwyM/vvf/2rab8SIEXjttdfauTSEkERKVLJJFARA9HXeFKM1pVHwQwiR6ZZ9jAghJCZ/tgiIrymNAiVCejcKjAghKuLP23S52X78y4EAwcAoVsxDMREhhAIjQkiPwzDBEWlA9Ka00HCIMkaE9G4UGBFCuhT5iDaxLcPbBNmINIp2CCEaUWBECAnTqngkgfMPtRUDSEP1o2eL1A6UPaWAipBehwIjQkiPJAZmvWaDg2/Vwpzw2IeCIUJ6MwqMCCE9D8O0cnJHQkhvR4ERIaRnClknjRBCtKDAiBASplVdjBSPO3/wfmBUmsjKMkYxWskYUD9tQno7CowIIT2Or/O1bx6jWJ2vKQ4ihMhRYEQI6VLERI1uE1rXx0geKFH2iJDehwIjQki4treldS5G3pQmH5WmZRFZQkhvRoERIaRn4jV2vo4SC1GYREjvQ4ERIaTLam3iibHXw7nuGd852DiH61PWiJBerV0Do88//xzbtm1rz0sQQki4Xz+SHgqyjBHFPISQWGJO8CGKIhYtWgSLxQKbzYYhQ4bgzDPPBMuyuPTSS5GTk4MZM2bg1FNPBccp/zJbsmQJDAYDVq1a1W5vgBCSePLh9lo7QHelLkYQheDDGBmjqP2OKJIipNeJGRgJgoD33nsPo0aNgtvtxgsvvIANGzbgwQcfxJYtW2C32/Hxxx8jKysL119/PS6++GIAwMcff4zdu3fjmWeeafc3QQjpORSBWGuHpRmTg6dow6g0Qkjvo7kp7bHHHsPq1avxyCOP4KOPgmnqJ554Ap999hlOO+003Hvvvbjiiiuwc+dO3HPPPTj55JNx8sknt0e5CSEkMkOS9JAV3J1YEEJIdxN3H6OioiI4nU643cEvm5ycHNx555146623UF5ejlmzZsFgMOChhx5KaGEJIR2jNYka+fxDnd+WFsR5HdF3kKWIGEbZekbZI0J6n7gDI4fD9yUT2p8IANLS0pCamgpBEGC1WmGxWNpeQkIIiZfASw91sQIjQgiRiTswqq2tBRAMjBiGQV1dHZ566imcccYZ8Hq9ePbZZ1FRUYFHHnkksaUlhPR4io7frT2Jf9ZrQEPGiBBCZOJednr06NFYv3699Pzmm2/Grl270L9/f9x+++244IILwHEc7r//ftx0000499xzMXr06IQWmhBCopIFRqwYfKw2szUT8oyJvJEQ0gvEHRilpaUpnp9wwgm47777MHLkSMXrgSH8Dz74IF5//fW2lZIQ0mlatzpIJ3cy4oPB0MFBl3RiQQgh3Y3mwOjTTz9Fenq64jWGYWC1WrF7927s3r077JiRI0ciNTW1zYUkhPQiCejDzQg8RACGsWejyTQEaHAmomSEkF5Ac2AUqb/QY489FvU4hmFw7733xlcqQghpi0BTGqePuau8tYxhlC9QSxohvU/MwIjjOOzYsQMAUF9fjy1btqCkpAQ6nQ67d++Gx+PBiBEj2r2ghJCOoxh5r3HsvvKYeK4lJn5V+8CoNJXRs4QQEk1co9J27dqF+fPno6WlBQCwfv16zJ8/v10KRgghrebPGDGsLvqSHypoFRBCereYGaMdO3aA531/fe3fvx+iKGLHjh1ITk5G3759UVFRgdWrV2Pw4MFhx9psNuTn5ye+1ISQHkuM+CQOgc7XrA4ILpumHvREiYQSnskihHR5MQOj2bNnw+l0Sul0hmFw5ZVXKva5/fbbVdPtDMPg7LPPxuLFixNUXEII0UDqY6QMjGKhMIgQEjMweueddyAIvm+Wbdu24Y477sAbb7wBq9UKAHjxxRfx6aef4tVXX1X8dSWKIp577jl88MEHFBgR0u3En6ppbXJHRDsEJIGmNE4Hxhtj3xDxNr0RQnqWmIFRQUGB9Li6uhqiKGLAgAHSMPyzzjoLb731Furq6jBhwgTFsccccwxWr14NnudVlxAhhJBQouJxK8OtQOdrVvkVp9qS1rorEEJ6qLg6X+fm5mLBggUwm83SaxMmTMD06dPR3Nwctv+IESPw2GOPUVBECOlQotTHKM7vHoqSCOn14pr5OhAYyen1eixdulR1/+LiYhQXF7e+dISQTqHI2mhN2sh21DrEX7pYggMSRvD6TsvFPbk/BUeE9HKavjX++Mc/tvoCDMPgP//5T6uPJ4SQeImCbFSanNpaaVECIRqURkjvo6kpbfPmzXC73eA4TvP/brcbmzdvpmY0Qkh8xASsCSJN8BhfxoihrteE9HqavzUeeughDBw4UPOJ9+7di7PPPhsvvvhiqwpGCCGtxgcneCSEkHjE1fk6HjQxGiHdlzJpo3FJkNZeq9VHRiGbx6gtX0X0LUZI79OqP6dOOeWUiNtmzJiB2267rdUFIoT0bmKEx3GJ0MdIS6BDf9MR0ru1KjAqKyvD5ZdfjrS0NOm12tpavPbaazjmmGMSVjhCCGmVwHD9OPs4MgxCojGKkgjpbVrdAP+HP/xB0edo3759eO2116Jmkwgh3ZDm4fqyh/GM1tewb1zD/wGp8zXD6sGAj7orNfsTQuTarY8RIYR0GvlaaXKaYiAKlAjpzSgwIoR0La3MPCkOkpYEadt0IZRMIqT3aXVgROlnQkhXxIjBprPWzHxNX22E9G6t7mN02223wWQySc+dTidEUcS///1vzJ07NyGFI4R0DnmfnlZ0MUr4APx4zsfKAiOwOkXLGMU8hJBYWhUYzZo1S/X1QYMGKUaqEUJIvMQoz7RgQgOjeI4NG5VGCOltWhUYPfTQQ4kuByGEJISUMWJYMGzs3gKhTWeUVSKkd9Pcx4j6FBHSe4gRn0Q5Rmxdr2lNu8aRxZECI3+2SP7NRd9jhJBYNGeM/vznP0Ov12s+scfjgSiKmDFjBtauXduqwhFCSLykprQELGBNcRQhvY+mwChSnyJCCEk0RcfvVvT3CWSMtC4gyyhzSpRVIqSX0/TNEW+fIrfbjccffxzXXHMNdcYmhHQoqSmN82e4Kc4hhMQh4RM8VlZW4oorrsDKlSvx3XffJfr0hJCOIO8u1IphWvEdEXvveMogLQHS2skdFcP7KaoipLeJa1Tak08+GXHb3Llz8fvvv2PBggUwmUx45ZVXMGbMmDYXkBDSe7Vm5DzrXw5E8+SO8kCI4iBCer24A6O0tDRwsk6NNTU1SEtLwx//+EcsXrwYo0aNwuLFi5GcnJzwwhJCSCyho9LkKPAhhMQSMzD64YcfUFxcLM1y/eqrr2LgwIHS9mHDhuGVV16BzWbDs88+i+TkZFx33XVISkrCtGnTcOaZZ7Zf6Qkh7UIxi7Xm4frqx8dzXFv2CQiOSgsM129DNESBFCG9TtQ+RoIg4Oabb8aFF16I3bt3xzxZcnIyHA4H8vPzUVdXh0WLFuHBBx9MWGEJISQWVhQAxDMqTf0xIaR3ihoYsSyLp556CjqdDhdffLGmYaxmsxl33HEHXnjhBTzyyCN49913E1VWQkgvILZx0TWp83UrFpAlhJCYo9KKi4uxatUqXHfddTFP5nK5cO+992LXrl0AgKFDh6KpqantpSSEEI0Cna8Do9KYNiwiSxkkQnofTcP1OY7D3LlzlVP+h9i+fTtmzpyJVatWYceOHQkrICGkE7RmZkXF8e2yqybROl+rouiHECKTkHmM9u3bh0svvRQpKSl45513cO655ybitISQXkjZkhZ/2BTofK15uL7i4PgPIYT0LJq+Oex2O2pqaiJuLywsxFNPPYUpU6Yo+iFFyzC1Bc/z+Ne//oXPPvsMFRUVyMrKwsyZMzF//nzodMG3dPToUTzwwAPYsGEDOI7D9OnTcccdd9BUAoT0YGzUPkbxRT40vJ+Q3idmYFRWVob58+cjLS0taufrE088EfX19bDb7QB8wctbb73VLkuCOJ1ObN26FVdffTXS09OxefNmPPnkk/B6vbjpppukfa666irodDrcc889qKmpwZIlS9DU1ISlS5cmvEyE9CStGq4vOyquTE97Dddv1VpphJDeLuo3hyAIuOKKK5CSkoJ77rkHp59+etSTPfzww3jnnXcUAdQtt9ySmJLKWCwWvPrqq9Lz6dOnY9euXfjiiy+kwGjVqlU4fPgw1q5di/z8fACA1+vF4sWLceDAARQUFCS8XISQzscK8S0iK8dQmERIrxf1m4NlWTz88MMYM2YMDAYDAKhmjQKvzZ07F7NmzZKOzcvLQ3Z2dqLLrEqv1yMjI0N6vm7dOowZM0YKigDgzDPPxOLFi7Fx48Y2BUY6XcKXmAPHsYp/SWRUV9rFU1csG/zd1nGs9JzjGE2feU5+DMtGPYZjGWlfnU65L8cFt7Gs79piSPmilsPflMbqddDpWEW59PrwcimuxzEQvaKsbFy7/L73BPR7qB3VlXZdoa5i/kl17LHHKp5fdtllYFk27LWPP/4YNpsN/fr1g9lsTmwpVfA8D4fDgdraWqxZswY//fQTnnvuOWn77t27MXXqVMUxOTk5MJvNOHjwYKuvy7IM0tIsrT4+Fput/euup6C60k5LXZnNBulxSmqS9DwpyajpM1/T7JGOSbaaoh5jTjKA9+dmUlOTYDIGv4oaXXzwPMm+87g9vKJ80Rj8pzIlJSEtzYLkZCPMzW7pWkkmvWJ/W5M7eD2LCc4GB4xG3z5paUnQ61q5GG0vQb+H2lFdadeZdRVXrnnBggURtxkMBixZsgTvvPMOpk+fjssuu6xdF5H99NNPsXDhQgBAWloaHnnkEUUQV1dXh9TU1LDjrFYrmpubW31dQRDR2Ghv9fGRcBwLm82MxkYHeF5I+Pl7Eqor7eKpK4fDLT2ur7dLz1sMHOrqWmJeq6HRIR3T1OSMeozd7obD4QEA1NW3wGSQBUay8zQ3+87j9vKK8kXDe3zndXlE1NW1oKXZJR1bX2+Hy6j82mtqkl2vxek71uWBIIior7dDR3/lq6LfQ+2orrRrr7qy2cyas1AJC4wA4JprrkGfPn2wevVqvPfeexgzZgxefPFFJCUlaTq/1+tFWVlZxO2pqalISUkBAEyaNAkvv/wyampqsGHDBixcuBA33HAD5s+fD8CXUYrU7KdlBu/o5Wy/DzbPC+16/p6E6ko7LXUlCMEezl6vID3nBW31zPPBY4QY1xN4UdrX6xXgZYP7Kq7Ni/B6BfCy12Jh/BM8CmB9xwoh1+KU5eJlZREFEQx8dSEIvmsnfKKlHoZ+D7WjutKuM+sqoXPmZ2dnY968eZg3bx6+/PJLfPPNN5qDIgAoLy/H9OnTI25fuHChNAN3WlqalCE6/fTTkZaWhmXLluHiiy9GRkYGLBaLamaoqalJNZNECOkZ2LBFZONDcRAhvVu7LSZ08skn4+STT47rmLy8POzcubNV1zvmmGPwzDPP4PDhw8jIyEBBQQH279+v2KeiogJ2ux1Dhgxp1TUI6ZVaESnEOiTe6QDiGa4fCIxaMyqNEEK6ZeN5S0t434Uff/wRLMuif//+AICSkhJs3rwZlZWV0j5r1qyB0WhESUlJh5WVENKxGDHyBI9aWtFpuD4hvVu3/JNqxYoV2LlzJ0488URYrVZs3rwZr7/+Ov74xz8iPT0dAHD55Zfj1Vdfxbx58zBnzhxUVlZi2bJlmDNnTrtMOkkISRBZdqg1k+fHvVYaIYTIdMtvjhNOOAGbNm3C4sWL4fV6MXDgQDzwwAOYOXOmtE9mZiZWrFiB++67D4sWLUJ6ejrmzZuHa665pvMKTkg30V7L+bRWPKVh41wrLVoWiZYEIaT36ZaB0bhx47By5cqY+40YMQKvvfZaB5SIkJ5La1AixpPpSXDgxfEuGPgWOAzpsiVB4p9/yDditWsFhYSQjtUtAyNCSM8lRnkWyfF7l8HA27GxcF7YqDQ5WvCDEBJLt+x8TQhpX12sJS1mfGTgfZOuZjTvkTJGDKuPdogkWrBEgRQhvQ8FRoSQhBCVg/Bj7Ct7nMAgTAQryxjRUh6EkPhRYEQICSN20342Fnc1rM6jvidqo9JiJIAoP0QIocCIENKlyEfExZtNyq3/SXrMWFLbXhiKlAjpdSgwIoSE62IJo2gZLEYMX0+J6z8GXGpOexaJENJDUWBECIlK85xGcQzXV25vWxTGit6w13T5o1T3jZkAogwRIb0eBUaEkDBdLGEUFSuEB0YMZ9B8fNQJHltTIEJIt0aBESEkTFcLjKJloNQyRtBpG6pPCCGhKDAihERtLtM883Urjol3XzWs6Al/kVMPjGIt8UEZIkIIBUaEkHBdbobHyNralBYNQ4ulEdLrUGBECOlSFGuuadifo6Y0QkgCUWBECAnTffJF6n2MmAhNadRYRgiJhQIjQkj0QKgdOhnJ5yVqa6sdK2jvY6RG0VxGTWeE9HoUGBFCwnWRlJGWOZSktdFkGF1i+hgRQnofCowIIWG6SFwkiRYfcXFkjCghRAiJhQIjQkjUSEh7S5qo+rhNJ9VAdR6jeJrSIjwmhPROFBgRQsJoXgakC1DtfE1NaYSQVqLAiBDSpcTMNoVQm8conowRIYTIUWBECOkGIgdLoTNfi2AAltN+ahqURgiRocCIEBI9S6OxWU2Ma7i+/Li2NduFTvAosDqasZoQ0moUGBFCwnRmFyPFzNcayhHalCYw1IxGCGk9CowIIWG6WtfraOUJ7XwtsLqI+6olkii3RAiRo8CIEBI1M9OaICnWMfLms7YGYZzgVjwXmMiBESGExEKBESEkXDcarm9xVyuei1EyRoQQEgsFRoSQLivQKTxinCaKsLiqFC9Fyxgxag1nIe1r1HGbkN6NAiNCSBjlqDGNx8ibx+JJOLUhOZXkroEutCmNbX3na4ZhutXkloSQxKPAiBASrhvEBozgxeR9T4W9znPGTigNIaSnoMCIENKlaJ0Pyehtkh6X20ZKj12GlMgHaRiVRk1phPRuFBgRQsJ0g4QRWJEHAHhYIw6lT5Zed+ujBEYxUEhECKHAiBDSDoPQop9QMYljK6/A+AMjkeEgMMElQFyG1Fae0V8e6mNESK9GgREhJEx3CA5YUQAACAwHURYYuaM0pallhKjljBAiR4ERIaRLka/bFnikFqgxCGaMRCb4VRYtMNKC+hgR0rtRYEQIQbQGLa3Zo3jWOFNsbmV2KtDHSAgLjGytOh8hhAAATRFLCAnTDVrSFH2MnPpU7MmaCg9nAWTNaoQQEi8KjAghXYvG4fryjBEAHMycAgBIjnJqaiYjhMRCTWmEkC5PLT5i/J2vxZCvsbYEPxQ3EUIoMCKERG0609qqFlfrmxjewTpeoRmj1qIsEiFEjgIjQkgYsRtM8SjvY0QIIYlCgREhpEtRdjESw1/0S1TGSI6hua8J6fUoMCKEhMUdWtcri3RQrEMUp29lciqYMWrb1xiFQoQQOQqMCCHdUqSMEXUZIoS0BQVGhJCuRWWiyKij0qiPESEkgSgwIoSE6Q4TPEbMGMV7IibCY0JIr0SBESEkaqeg1gzXj7mMSAICLxqVRghpDxQYEULCdIfh+u0xKo0QQigwIoR0KapBmUoGikGEUWlx9r6mljRCiBwFRoSQ8GCkFePp48kxxdXsFgFljAgh7YECI0JImK7fkCZfK62Nna8JIUSGAiNCSJciahyun7CMEU18RAiRocCIEBKuG6SMIo5Ka0ucQ0ESIb0eBUaEkKjdiDTHSCqZnsjXa3vkRX2MCCHtgQIjQkiY7jBcP1FrpRFCiBx9oxBCuqxAgKaWYErUzNc0XJ8QIkeBESEkjFoH6JjHyLJM0TJOoc1orW1Vo7XSCCHtgQIjQki3FDFjRB2oCSFtQIERIaRLUWSUomSTErVWmqIpjWIqQno9CowIIWESMWqsvbGgUWmEkMTrEYHR8uXLMXToULz99tuK148ePYoFCxZg/PjxOPbYY/H3v/8dzc3NnVRKQrquhMRBYoTHUXbzPW/dxYN9jHrE1xghpIvQdXYB2qqurg4vvvhi2OtOpxNXXXUVdDod7rnnHtTU1GDJkiVoamrC0qVLO6GkhJDWUstgJW7m67YdTgjpWbp9YPT4448jNzcXO3fuVLy+atUqHD58GGvXrkV+fj4AwOv1YvHixThw4AAKCgo6obSEkHio5ZKyGn+H1VkOg7fJt08bh+sTQohct85Bb9myBe+++y4WLVoUtm3dunUYM2aMFBQBwJlnngkA2LhxY4eVkZDuIfIQeq1NXRpb0tTa0jQzehoxquxNDKxZDwPvAAB4WZP2ExBCSAzdNmPkcrlw55134sorr8SAAQPCtu/evRtTp05VvJaTkwOz2YyDBw+26do6XeLjSY5jFf+SyKiutNNaV5yOBcsGcy0sx0jPWZbR9JnnZMdwUY4RBFFxLU7HKvblODbsPDp/+SzeOjAAPJwZFSnFsBsyYDf3BSsbTsZxyvPJ34tamXQ6Lng9fz1F25/40O+hdlRX2nWFuuq2gdGjjz4Kt9uN6667DjU1NWHb6+rqkJqaGva61WptUwdslmWQlmZp9fGx2Gzmdjt3T0N1pV2sutI7PDCbDdJzq9UkPTcZOE2f+eTkZpjNviyOxWKMeAwviIprpaQkIS0tKXieGoe03ZzkO48XLMxmA6z2FgCAPSkHpYUzffuEnN9iMSiunWwxwWx2A4BqmTxgpOtZrSZUN7lhNOoj7k+U6PdQO6or7TqzrrpUYOT1elFWVhZxe2pqKlJSUvDll1/i1VdfxcsvvwyzWb3yeJ5XneiNYZg2TQAnCCIaG+2tPj4SjmNhs5nR2OgAzwsJP39PQnWlnda6anF64HC4pedNjU7pucBzqKtriXmt5ubgMS0tuojHCIKouFZDgx0GJtie1tTkkLbb9Szq6lrQ0OA7N9vi+yPIzlkV51C8F4OyvM0twXKplSlw7sB7AACXywNBEDW9796Kfg+1o7rSrr3qymYza85CdanAqLy8HNOnT4+4feHChTjrrLNw6623Ys6cORgyZAhaWlrgdPq+zNxuN1paWmCxWGCxWFQzQ01NTaqZpHh4ve33weZ5oV3P35NQXWkXq668XgGCICr2DzwXeFFTPXu9onQMH+UYXlBey+tVlo3ng+cRBN95AuUxuhsAAA6dTXEOucAx0nPZ+dTKJMjeK88Hrxt6HqKOfg+1o7rSrjPrqksFRnl5eWGjy0I9+eSTaGxsxNNPP42nn35ase3uu+/G3XffjZ07d6KgoAD79+9XbK+oqIDdbseQIUMSXnZCehIRvtFfA2o3gIWIlqNGMOYUmKfOBWPs+OalQGdwi6sKAODUp7TLdWhEGyGkSwVGWpx//vk47rjjFK9VV1dj4cKFmDdvHqZMmQIAKCkpwfPPP4/Kykr06dMHALBmzRoYjUaUlJR0eLkJ6W76125EisPXtC34ug7BW/Yb9IXHtut1FaPbZMPjcut+RKrjMIDogREt60EIaYtuFxjl5OQgJydH8VppaSkAYMCAAZgwYQIA4PLLL8err76KefPmYc6cOaisrMSyZcswZ84cpKWldXi5CenKQudPFBGcQPFg35Mx0LsfQs1BgPdEO4vsUeQx+GrX0iLVHhxN2mjKibInIYS0Xo8dO5iZmYkVK1bAaDRi0aJFWLFiBebNm4cFCxZ0dtEI6frE4CKtjUn9wVhSfa/z3k4rUqA8O/ueDp6juYsIIe2j22WM1ETqmzRixAi89tprnVAiQro/VrZ6PcP6vipEgW//C4vyzFPgX1G2BEg7fm1RMxwhvV6PzRgRQlpPhChlaASGA1j/shtCZ2aMhGB5omDj7GTUluk7CCE9DwVGhBBVUoYGDMD5szRRAiPFMiJxLPOhdWd5BisqinMIIW1AgREhJJysj5GvKc0XjIh8BzSlxShPrIxRWxJAFFMRQigwIoSoYiFrumIDGaP2D4zECE+CGaPoX1sMhTeEkDagwIgQopgvCPDFI/KMkaamNM3Xat1xHZExIoQQCowIIWFEMaRPj9SU1nmdr1lo62PUls7U1BGbEEKBESEknCgfHh8crt8Ro9JElcfyCSdjZozivB7FQoQQOQqMCCHhxGBfIpGVD9fvpM7XCGnai7YfBTqEkDagwIgQEtbPh5EFRrzIaupjBM3D9VvXyUjrPEbUHEYIaQsKjAgh4WTD8rvCcH15U1rsUWnxoTCKECJHgREhJJwYzAyJiuH6HdD5WtHJKPhEcx8jyhgRQtqgR6yVRghpo9DmrECzFViIjHzm68gZIxGi6uOw/do4XJ9mvm5fgiCA1zD6UBAYOJ0c3G4XeD6eqc57H6or7eKpK5blwLJswv8YosCIEBKG8d8YpSCks4fry0fJIdZaaXGeW/al2puTTaIoorGxFg5Hs+ZjqqtZCILQjqXqOaiutIunrliWQ3JyKsxmS8ICJAqMCCFhQidT7Njh+vLMU+CBAMb/LFZTWltTRn3SzG06vrsKBEXJyWkwGIyabjIcx1AGRCOqK+201JUoihAEHk6nHY2NNfB4XEhJyUjI9SkwIoSEE9QzRp02XF8I6fMURdwZoxD5fa2YNDIbyabe8/UoCLwUFCUn2zQfp9Ox8HopC6IF1ZV28dSVyZSE5mY9mpsbYLWmgmVj/eEUG3W+JoREHK4vMKyvU5CGPkaqMzNquFbociSqBOUouajiDIxCd2cYBrmZFlhM+vhO1I3x/tGGBoOxk0tCSPwMBhMAUfoctxUFRoSQcLyyo3OgKa2z+hiJssBIiDVcvzd3FGojqjvSHSX6c0uBESEkTNiCrVJTWscO1w8kkxj/dQUwQILnMSKEEDkKjAghKmPogxkjEdA4XF/9cdQdtRI0DtVHK/56pEiKdDK3243KyorOLgbxo8CIEBKOD80Y+ZvSOiJjpFoer7I8UbQlzqGmJBKPwJDy7dt/wYMP3iO97nQ6pcc//fQDFi26CS0tkadB+OmnH3D++WfB6+2k3y+i0HuGXRBCNGPEwKg0399OgSVB0AF9jFQTSnFljBJbHtIzXXjhOSgvP9qqYy+//ErU1tbAZkvBDTfchPLyI/jss7X429/uRl1dHebMuQLXXns9pk8/Az/++D327t0DiyVZ80irp59ehlde+U/M/XJz8/DGG++26j2QyCgwIoSEidzHqHOH62vKGMUZGVEc1Xudc84sXHrp5WGvL1p0E7Kz++Gmm25TPc5mS8GOHb/httv+ghEjiqXXeZ7HP/7xd9hsNkyZcjIAYOvWn3HssZPiLltx8WjcccddEbd//vknWLv2w7jPS2KjwIgQEj5cP5ChAefbqKWPkca+Q6HLhWg5jumgjBEFSb2LzWbDgAEFitdKSw+jtPQwFiy4KWyb3OTJJ+CGG27CkCFDsGvXTgC+vkJ9+vTBHXfcBbPZjLq6Ovzyy1ZkZ/fD6tXvQBCCH/a+fbORmZmJffv2Ys+e3QCAdes+g8HgmybCaDRFvX56enrr3jSJiQIjQkg4QX3m684erq+tjxGFNyQ+L7zwLFas+Lfitdtvv1l13zfffA+lpYfx9tv/AwD8/POPqKqqgtfrxb333gkAeOKJxbj44suwe/dOcJwO+/fvw4ED+yCKwIED+2A2J2H69NNhsSQrrnvvvXfCbDbj/PMvhsvlxMGDByKWuba2to3vmkRCgREhJEywj5E/EOE6cEkQxXB9UXHd9sgYUYdrAgB5ef3x8MOPRdy+f/9e3HnnIgBAWlo6RowYBQBoamrEd999C4ZhpNcAIDnZijfffB0jRozEk08+J83mfN55p+Pcc2fh6quvRVNTE8488xxs2fITHnjgH3jjjXfBcTq8886b2L59Gy6//MKoZc7NzUvAOyehKDAihIQ1ZzFCcOZrEdA4j5FsjbMo7WNhMwNoKB8TR8aobcPS2nBsDyWKInghwk+JAbx8xy9zwbFMQgJa+Tn0el3UQKO5OTiqbPDgIRg8eAgOHjyARYtuRlZWH1RWVmDduk9w9dXzUFJyIt544xUcPXpE8bvg8XhQV1eLnJxcAIDVaoXVapUyQ337ZkOn892WjzlmIp544inp2NLSw7j00ll488330K9fDhwOB+z2ljbXAQlHgREhJJyoPvM1RBGiIIBhO3amD1Hw+MsT+7osZYASRhRFrN92FLWNTtXtLMso+s10lAybCSWj+7U5OJIfv3//Ppx88nGajhMEAW+//T8899zTmDnzfAwZMhQPPXQvzj33fPzf/y3CxRdfhp9//hHFxaPx66+/oLGxAenpadizZxcEQUBBwcCI53a5XP5/lU1pFRXlAICyslK43W7pdY/Hi+zs7HjeNomBAiNCSJiIo9IAX9aINbTj1cXwR/FkjEhC9dQw0+v1StkZAMjP74/HHnsy4v579+6R+h0tX74En332Cf7+93/gpJOm4rPP1gIAZs26EAMGFODFF5/Dbbf9HZmZWZg16wxs2fIzpk2bhl9+2QqTyYQhQ4Yqzl1TUw0AuO66OUhNTUNOTm7EprS//OU6xfOxY8fjySefa10lEFUUGBFCJGZ3LTjBBb2zDoCsT48sMOKrD4DRGcHassAYkhTHs4IHSe5qmFkD+OrwZjc2LVd6bPLUQ8c7wNa7wcMsvW5oqkey09dsYRZ14KsFsM2VvvIgdmBEGaPEYRgGJaP7RWxK66wV4xPRlOZyuWA0BhfNFUUoMjGhvF6P9PjKK6/GVVfNhdVqxbPPLseOHb/h00/XAwDGj5+A8eMnSPsOHz4S69Z9imnTpuGrr77AmDHjpYBs5cqX8Oabr6G2tgYAMHr0WEydeipWr16Fk08+Bfff/7B0ntCmNNJ+KDAihAAAsuu3YuTR1YrXBIbz9ZHggl8VjvceBAAwZhsss/8FRufLHokiMOHAClhdvpS//dfwa3A5w8Gc9lekN+/FuMOv+F7cD9hl++T6/w+w7wACt6/2zhjRiLZwDMNAx6nXi45jW7fESycTBAF2ewuSk5Ol10pLD8Xs7Bzw7LPLsXr124rXTjopOFdRdnY/vPXW+wCAU045DcuXP4HvvvsW27Ztwd/+dre0n9VqxaWX/gEmkwmPPfYw5s1bAJ1Oh5dffglZWVlteYukDSgwIoQAAJJdvqyMlzVA1Jnghh6VthEAAIZhoR8xDd6DPwMAxJY6iI5GiM5mMMnB+VQs/nN49MnSfCwAAIGH6GiEUFcGDoDFVQUA4BkdGGMyONmN1+MVpA69DMPAZODACyJcXuBo6piY7yP+UWnx7U+6v5qaavA8j4yMTADAeeedjwkTJsFqTUZh4WAAvpmxTzllOubPvwGHDh1EcnIyKisrkJmZhauvvhYXXHAJ7rjjrygqGoY5c+ZJ537ooXuRnp4hPT/77PPw0ksv4Pbbb0F6ejqmTj1V2jZz5gUAgI0bNyjKt3//PowYUYz586/GL79sVWy76KJzpcfUjNY+KDAihAAAWH+/osNpE+EccQ6OVCtHvJhK/giU/BEA0PTitYDXpRylJgpg4Qtofi2+EVOOLZI28fVHYP/f36T5iALXqrAVI3naHORmBf9y37qnGvuPNgIALCY9Tjs2H0drWrDpN22LbNLwexLLzp07AAD9+xcAADIzs7Bs2eP4/vtNePPN1bBYgp9Hr9eLW265ERkZGfjXv5ZCr9cjPT0DX3zxOcrKSnHeeRcgNzcPOp0On3zyMX7//VesWPGKdLzRaMLUqafg7bffxOzZV8BkMkUtm91ux5EjpRg+fAQ2bFiP88+/CBdccEnYfm+++RoOHNifgNogoSgwIoRAFEWpw7XIcOFD6kVRGXBwOsDrUiwqy4jBviah8w1Jo9p4L0RR2bk70S0xbZr5mmKqXuHrr79AVlYf9O8/AACwevXb+PzzT3DnnfcogiIA0Ol0uO++h7Fw4TzcdNMCPP74k7BYknHOOTMBAC+//BLeeut1TJ16KlategN/+cutUtYJ8C0Qu2bNarAsi7feegNTp56KQYMGI5JvvvkaLMtKS42kpKSqzoBts6W0sRZIJB075pYQ0mWxonzuoujhSnBRWdkSIbIgKWwixpB5kFgpCNP2FaR1uREg/lFUFAv1Lg0N9fjqq3WYNu00MAyDrVt/xhNP/AtnnXUuTj/9LGk/r9cr/TEwdOgwPPzwEuzZsxuLFt0Ml8sFg8GACy64GMuWPQuWZfHGG74sUWVlhdSZevv2bbjjjr9iwIACrFjxMoxGI26+eQF+/XW7oky8//eIYRh89NH7KCk5CUlJyoENpONQxogQAgBg/M1gWmaXRiADJFs7LVrGSL6/KIpgEHn4vRjhsVbUlEaiefHF5+ByuTBz5gXYseN3LFp0M8aMGYdbb/0b3njjFXAch/r6elRXVynWIxszZixuv/3/cO+9d+L555/B6NFj8PXXX+Kzz9YiJycXixc/gT17duOVV16C2+1Cbm4+li17DLm5eXjssSeRlZWJf/1rKf7yl/m47rqr8ac/zcWoUWPw22+/YsOG9bBabdi4cQO+/36Tot/QihX/DluuJGDs2PHtXl+9EQVGhBAA8owRFzsiUZkJW2qKAwOEZIIY2ag2iLwsY5T4UWYUF5FozOYkXHDBxcjP74/y8nJMnXoqbrjhJuh0OmzY8A1+/vlHMAyD4uLRmDHjTMWx06efDkHg8fXXX+L111/G6NFj8be/3Y1p004Dx3GYPPkEnHvuTJSVleL66+fiuONOwN///g9p9NuQIUV44YWXceedi8DzPCorK/D66ythNidh/vwbcORIGWbMOFMR8Fx44SW48MJLw97HG2+8iv3797ZvZfVSFBgRQgBA0ccolAhlkxPD6SAiZFFZf5AkLSMix8q+anivlF0SGW3DvePJHMWfMaJIqje55prrpNmls7OzsWjR36VtTzzxdMzjTz/9LEyYMAk333wbMjPDh9SnpKQiJSUVzz+/EoWFg8I+j9nZ/fDUU8+DZVnodDqcccbZiu0eT3C+pKeffiFiOf7610Uxy0pahwIjQgiAODtESxkjeVNalCxQyMzZbOjM2nKKtrT4G9NCw5xuOM0OaUcsy8JsNsfeMYrMzMyY+0TrYG0wRJ45Xq/XR9xGOgZ1viaEAICyeStmU1qgz1B4U5pqsKMIjPjo+7YRNaURQtqCAiNCCEQR2rM4gDQTtny4PvzNar7ASnkAwzBScCTKMkZiOwzXD42M4omTKKgihFBgRAgBIO88zbVquL6UBYq0nplsZJr8WprE0aQW75caBUOEEDkKjAghAILD7QUtcwupNaUJMUaayUayRctOyYOyVmWTKNAhhLQBBUaEEADKPkYxEzRROl9H6jckDdkXeFkQlvg+RmxICiiuEW0UVRHS61FgRAiBCFFzFgcIBjny4fqMbDZr1WCEDRzDK2a+FjU0k9HIMkJIR6HAiBACAMq5hWJRyxgJMUaayZrSog7tl0VBrRitTzNfE0LahAIjQgiAkHmM2jBcP1IfI0ZjH6O2oriIdCVerzf2TqRLoQkeCSGAiKjLdIQFSqzKcH1B3jymcg15HyPEkZ2KU5syRhRUkQT73/9ew9tv/w+vv74KOl3kiR21+vjjD9DU1ISLLgpfJiRejzzyAMxmM2644WbV7bW1NaipqW71+QcOHASdLhhmvPDCs5gwYRLGjBmr2O/rr7/Evn17cMUVfwLHJf6PpXhRYEQIAQCwkM98HWO4PhdluH7EprRgYBR3xiiOJjWKbUg8nE4nnntuOXbs+B1PPfV82Pbdu3fhueeW45dftoLnBYwaNRrz59+IIUOKpH1efvkl1NXVqgYY33zzFXJz82EymeD1CmHbA9xuN44cKUNjYwMaGxtQW1uLiopyVFZWYOHCW5CcnIy6ujosXfoYJk8+AQcPHgg7R3Z2PxiNRlRXV6OlpVn1Omlp6bDZbACAQ4cOwmq1RSzTe++9g+effybi9ljefvsD9OnTV3q+Zs1qpKamhgVGr732X3i9Xlx11ZxWXyuRKDAihACI3RSmoLaIrLRWWow+Rnz0PkZt7WhNTWlEC7vdjo8//gAvv/wSKisrMGrUmLB9du/eifnzr4bFYsEFF1wCl8uF1avfxoIFc/H88yuRn98fAJCcbMUzzzwJvd6AefMWSMeXlh7GL79sxW23/T3s3KGqqirxhz9cBI7jwLIs9HoDBg8egvT0dNTWVsNiseChh+5BY2MD1q79EGvXfhh2jmeeWYHi4lF46qkn8MknH6le58Ybb8bFF18GABAEARwXOWt73nkX4MQTT1bdtmbNavzvf69hxYpXI54jPT0jxrsO1NE23HTTrTH37SgUGBFCAIRkfDT3MdK4Vhrkw/Xbu48RRUYkuurqKlxyyUy4XC5Mm3Ya1q37VHW/xx57BADw1FMvIDc3DwAwYcJE3HLLjXj++Wdwzz0PAgBmzrwA9fV1eP75Z2CzpeCyy64AALz77iqIooiHH74fDz98f8TyvPLKWxgwoAAff/wlkpOTsXjxgzh69Cgee2yZtM+TTy7B9u2/4H//W42cnFzpdVEUceedt2H37l0YPHgIAOCuu+7DXXfdp3qt5uZmKdvkdDrhdDrDsk/p6RmwWq1IS0uD0WjAxx9/iPPPv0ixT1qaL+gZOLBQ0Vz2008/oKmpCSedNDXi+5X76KM1AIClSx/D0qWPqe6zbNmzGD16rKbzJQIFRoQQiIjexyiU2nB9xDo+wszXWkaexTUXEcVFJAZBEDBhwkRcfPFlOOaYY1UDo8OHD+GXX7bi7LPPk4IiADjuuOMxcGAhvv32a7jdbmlB2KuumoP9+/fiP/95HmeccRY4jsPq1W/jwgsvxaxZF4JlAY+HV/SheeWV/+CLLz5HVlYWACA5OTmsHKIo4qmnlmLVqjdw55334uWXX8L8+TfCarUCAP7976fx3Xff4vHHn4LJZIr53r/44rOwIG3jxg2K57fccjtmzrwQALBly8947LGH4XK5MHv2H6Keu6mpCffffzc4TodJkybDZDLB6XSioqIcAMDzPGpra3Hw4AGYzWYkJ1uxevXbOOWU0zB9+pnSeTiOAc+LqKyswKOP/rPDF9alwIgQAgiC1DdH0LJ+mVrGKI7h+lHnTJIP129Fw1pb4iKKqXqHPn364uGHH4+6z9atPwMAxo+fELZtzJhx2L9/Hw4ePKDoa3THHXejtPQw0tLS8dRTT4BhGPzpT3OQkpKKJ598DOvXf4033ngXgC9b8+23X+Oss85BUpIFzc3NUkfn5uYmOJ0OHDx4AB6PB9u3b8UDDyzGkCFFePnlFZg370945JEleOONV/D+++/iH/94MKzfTiTnnDMT55wzEwAwZ84fUVg4CH/7290AfAHjiSdOhE4XDESOP74EF154CZ55ZhmGDh2mWh8Bjz76T9TV1eLpp1+UgrTfftuOG2+cJ+3zn/+8gP/85wUcf3wJhg8fiebmJsyffyOys/tJ++h0LLxeAbt37wQA6PVt77QeDwqMCCGKvkKillk81PoYxVgrjVHpY9TVmtJoIslwoigCXneEbSzEKB2K243O0O5NpqWlhwEAOTl5YdsCN/GKiqOKwMhkMmHw4CGoq6vDW2+9gdmzr0BKSioAoKXFrsjofPjh+2hsbMQFF1wCAPj66y/w4IP3KK5z+eW+rM3LL7+JgoKBAHzNegsXzsdll12A9PQMLFv2HIqLR0nHyAOsSHJz86DT6eDxuGEwGKXXPR7fzzk0Q3PddQuxdevPuO++u/Cf/7wGmy0l7Jwvv/wSPvtsLW6+eRGGDRsuvT5+/AR8880PAIBZs87EH/5wJS644BI0Njbg4ovPw9lnz8TPP/+II0fKcPXV1yrO6XZ7AAAGA2WMCCEdTJRlfmJlcQCoDtdnhBizWQea30QebDyTSQKaZscmiSeKIuzvPQChYk9nF0WB6zsE5nP/1q7BUVNTIwD15i2Lxfea3e5QvM7zPL788nOccsp0LF36DPr3L5C2HT16BIcOHYTd3oKkJAtSUlJx8cWXSR24zzzzHJx55jlwOByYNetMjBhRrOhj5HQ6sWHDN3jnnTexY8dvOP/8izF37jypLAFqAVao119/B3l5+aitrUVaWpr0emDOpdAMjcFgwB133IW5c6/Eo4/+E/fc85Bi+4YN3+C5557CrFkXhfVFiqSxsRGjRo3B1Vdfi02bNuCll55HU1MTFi78q6w8HtXytDcKjAghUv8gILBWmsbh+nF0vpaa33g+vhFwcQpdK420TW9dP473T0XBsuHBeyAgY1ll3Xz++ae49947ATA45ZTTFNt2794Fj8eDzZs34uSTT8Epp5wWtg/gm6eoubkJDkcLPvjgPZxxxtm45547sX79V0hLS8PEiZOxbdsWZGRkhgVFQDDAAoBHH30YGzd+izfeeDfsfXi9XjQ01CMtLV16zePxBUZqGZqiomG4/vq/YMSIkWHbhg8ficsu+yPmzp0fti2SvLx8LF78BADg9NPPgsPhwKOP/hMtLc24++57AQBut8tfHgqMCCEdLTDUHqy23stSkBOeMYo9j5EHLKItIiuqPtSsd97H2wXDMDCf+7eITWmBviAdrgOa0kwmMwDA4XCEbbPb7QCgCExEUcQrr7yEfv1yMGXKSYr9d+7cgYaGegwaNARffPE5Tj75FNVriqKIVav+55+LqAZPPrkEX331BU47bQYuumg2ioqG4uabF4DneTz77JN49tknFccPHTocL7ywEgDgcrnw2Wdr8Yc/XAmWZSEIgiI4qq6ugiAISE8PBkbyjFFNTTV++GGz4vwpKSkoKytFWVkp9uzx9f/59NOPwbIsBg4sxGefrVXsbzYnRRzuH2rWrAvhcDiQkuJrpisrK4XLFWjao8CIENLB5M1ggIZ4RG2tNDH6PEZSHyPZTbY9+hixFBglFMMwgN6ovk3HgmE6ITDqANnZ2QCA8vKjGDp0mGJbefkRAJCawQDgiy8+x969e3DXXfeHZTg++GA1CgoGYt68BVi06CZcccWfpKH1ofvV1FTjpJOmob6+HgsX/hV33PFXpKdnYPjwEbj33jtRWVmB559fCbPZLB3ndDrx17/eoDjnl19+Do/HjXPOmYVdu3bgjjtuwb///R9pbqGff/4RADBsWDAD5HD4Aj6DwYB9+/bivvvuillPDzzwj4jbsrP7aQ6MAOD88y/CJ598hCuuuBQulwtz5vgyUEaj+uevvVBgRAgBeF9bfuRARRkqScP15UuCyPoNqQZWgeY3ryt4iIZZtuNHkRFpuxEjigEAW7f+FDYnz7ZtW5GZmSUN4/d6vXjhhWcwZEgRTjtthmLfsrJSrFmzGnPnzsPEicchO7sfnnrqCTz2mDLbU1dXi+XLl+LCCy9BbW0N6uvrMWBAAVau/B+8Xg/uvPM2/Pbbdjz88OMYOnSYlDETRRH/+MffwfM85swJjv56441XcMYZ58Bms8FoNMDpdOCdd96SOjhv3rwR+fn9pQAQCAZGJpMJw4ePlDpNq1m58iU8++yT+PLLjYp5jLRoaWnGpk0bMW3aqQCAffv24L333sHHH38Ih8OOKVNOwvnnX4yKinIwDNPhgREtIksIka1z5g9eWjPBY8yZr/3HeJSBUSixrS1pFBeRBCguHo1+/XLx0UcfoLo6OMpr8+aN2LNnF84661wpOHnvvXdw8OABXHPNdYomPq/Xiwce+Af69MnGxRfPBsdx+MtfbsXmzRuxcuUKxfXef/9d2Gw2XH75lYrXKysrMHfuldi/fz+WLXsOy5c/gXvuuRMej0c6/1dfrcNdd92HrKw+AHzLkOzdu0eaaNJoNOGcc2Zh9eq34Xa70dLSjO+++xYTJx6nuFag2dBojD0fUmuIooj331+NmTPPxJNPPo7vv9+IefP+jD/+8VJ89tknOP/8i/Dmm+/hn//8F8aPnwCn09nhQRHQjTNGl112GX788UfFa2eeeSYefzw4N8XRo0fxwAMPYMOGDeA4DtOnT8cdd9yhOsqAkF4tpH+Q5qY0Pny4fsyZr/0ZIxGA2A7ZHQqMSCKwLIu//OUW3H77zbj22qukEWOrV7+NgoJCXH75HwH4Mj3//vfTKC4ejcmTS6TjvV4v7r//bvz66y94/PHlMBqN8HoFHH98CU477XQ8++xyZGRkSh2lp007DWPGjAubpDElJQUjR47C/Pk3wGZLwd/+djcWLpyPW25ZCIfDjv379+H++x/B5MknAAD27t2Df/7zfowcOQo7dvyGjRs3oLa2BgcP7kdtbQ0+//wTlJWVwuGw44ILLlZcK9B3St5M11bV1VVYs2Y1Pv74Q1RXV8Fms+Hqq6/BjBln4aeffoDb7cbf/nY3Tj11RlgTpN3eIvX16kjdNjCqq6vDBRdcgNNPP116rU+fPtJjp9OJq666CjqdDvfccw9qamqwZMkSNDU1YenSpZ1RZEK6Ln+2J1JQEzpIjYk6XD/CkiKssilNZDiAYbTNfB3PIrIUGZEEOeGEKXjkkcfx4ovP4ZVX/guLxYJTT52BefOuR1KSBQBw4MB+AMCf/3yNdFx9fT3uu+8ufP/9Rtx55z1hkyLefvv/oaKiHP/85304cGAf5s69Dnl5+cjLyw8rQ1KSBYsW3Sk9b25uwqBBQ7B+/ZcYNWoMXnhhJfr3HyBtt1gsaGpqxC+/bEVp6WFkZmYiI8P3f2HhIHz66cfYvv0XnHrqdMV0AoAviAEQdWHZeO3duwevvPIfTJ16Ku688x7FnEvTpp2qOjIvoLT0MDIyYq+3lmjdOjAaN24cTjzxRNXtq1atwuHDh7F27Vrk5/s+bF6vF4sXL8aBAwdQUFDQgaUlpIsLnXAxViQSZbh+zKY0f+fr9uh4DVAPIxK/aH1pJk8uUWSCQo0bdwxee+1tpKamAgA++eRjLF/u+yP873//B6ZPPyPsGKPRiH/+8zHcccdf8eqrK/HLL9uwfPm/VacGEAQBO3b8hh9//B7r13+F337bjuHDR+C++/6JqVNPDds/O7sfVq36AKmpqWF9f6qqKnHzzQvAsiyuvtrXH2nlypcAiNDp9HjzzdcwYEBBQltVJkyYiHff/Uh1agH5HzE//LAZe/bsgtVqg8lkxP79+/HRR2swa5a2eZESqVsGRqIoorGxERaLRbFWjdy6deswZswYKSgCfE1tixcvxsaNG9sUGOl0ie2a5eUF7DxcD/ZoE+x2NwQhOMqDc9TCdugbMIL6cNneiGEYHPEPE6aJ/6LTWleMvR4m+LI4LMvA5RUUc7T8fqhO8dxUa0c2AE9DFco+fNb3WnOZbyPLwSsI+GV/jeIatgYX0gHwtaXQya51tMaOZqdH2q/R7pauJYgiftlfg2a7J2zOmEj0emXAxbGMdKza766OZ6XtgVXCo6043hMJQvzhZOCexjDxZfR6okBQBACbNn2LzMwsPPHE09Js1Wp1ZbPZ8MQTT2PJksU47rjjVYMiwBcw3HzzAgwcWIjJk0twyy23o6homOq+AZmZmaqvf/31lzh48AAeeWSJtBDt9u1bsWHDNxBFEbm5ebj11r/F8c5j4zhONSgKVVtbg6efXibNH2WxWDB16qmYM+faGEfKr8Uk5P7MiN3wzlJXV4fjjgt2GuvTpw8uuugiXHfddVKEfOKJJ2Lq1Km45x7lDKBjx47F7NmzsWjRolZdWxTFhKfqj1a34IsfD6tuyz3yOfKPrEvo9QiJpME6EL8PnRNzvyR7OUb/tkx1285Bl6MubUTY61nVP2DQgXek5w5jJraOuqlV5Uy3mVDb6FTddtkM5U1j16E6/PB7heo2APB4Bbz5+S4AwMWnFkHXy4IiwNf1YO/efcjMzFYsEUHi53K5wHGsYr2xtmpoqJeWFmkLURTxww+bceyxk8K2hc5z1Fm8Xi9EUYxr4Vi324Xq6nIMGlSoaSHdWLplxshoNGL58uVITk5GXV0d1q5di+XLl6OmpkYKhOrq6hRRfIDVakVzc3Orry0IIhob7a0+Xo2BETFiQCo4vQ72FhcEIRirMqmnoMlmpYyRDMMAer0OHo+31/+lGkt8dcXANOhYDGR9f91xHAOLSY/GFpXPnjgIDYYrwNmVazKxSSnIGTgJqW6Vi2Uej0YLB9bTAoCBfsAYFBqtqpksvY6FUc+h2RHMJDEMA5tFD14QkZliwqGKZpgMHEQRyEw1ocnugY5jUVfXojhXRrIeI/qnIjPFFLYt4JghGWDAwN7igs1mRmOjAzzfM+fnUeN2uyAIAnhe1DxhI8P4Mms8L9DvoQzH+W7o8npsa11ZLLaETaQ5btyxEc8lb63oLAzDQqeLr654XoQgCGhosMPh4FX3sdnMmjPBXSow8nq9KCsri7g9NTUVKSkpSEpKwqmnBttWzzjjDHAchzfffBM33XQTUlNTwfO8amaHYZg2Z3zaY6bXAX2tSEuzoK6uJeT8KUDhrIRfrzvT6dgIdUVCtaauQpPwfVIjjArJU5+9N6r+Z0e9VjwK+yk7iBptviY0tfeZm2mJuA0A0q2+vzIDwRDPC73qs8Xz8d+tAzctCopio7rSri11FU9gH02XCozKy8sxffr0iNsXLlyI6667TnXbjBkzsGbNGhw4cABjx46FxWJRzQw1NTWpZpIIIYQQQrpUYJSXl4edO3e26li325fuD0wGVVBQgP379yv2qaiogN1ux5Ah4VOxE0IIIYR0fk+rVvB4PGGvrV69GqmpqRg8eDAAoKSkBJs3b0ZlZaW0z5o1a2A0GlFSEnnoJSGE9FbdcCwOIQn/3HapjJFWX331Fd58801Mnz4der0e77//Pr7++ms88MADUk/2yy+/HK+++irmzZuHOXPmoLKyEsuWLcOcOXOQlpbWye+AEEK6Ds4/L5Xb7aJRaaTbcbudABjpc9xW3TIwGjBgAOrq6vDAAw/A4/Fg6NChWLZsmaJ/UmZmJlasWIH77rsPixYtQnp6OubNm4drrrkmypkJIaT3YVkOZnMympvrAAAGg1HTIBVBYFrVcbs3orrSTktdiaIIQeDhdNrhdLbAbE4GyyYmMOqW8xh1Jp4XUFurPuS3LWiklXZUV9pRXcWnN9eXb+LcWjgc2qczYVm2Swzx7g6orrSLp65YlkNycirMZkvUYD493dI9h+sTQgjpHAzDICUlA1ZrGnjZ4sCRcByDlJQkNDTYKRMSA9WVdvHUFctyYFk24ZMuU2BECCFEwrIsWDZ8maVQOh0Lk8kEh4Pvddm1eFFdadcV6qpbjkojhBBCCGkPFBgRQgghhPhRYEQIIYQQ4keBESGEEEKIHwVGhBBCCCF+NI9RnHyTSrVPlXEcK63uTaKjutKO6io+VF/aUV1pR3WlXXvUFcsymof1U2BECCGEEOJHTWmEEEIIIX4UGBFCCCGE+FFgRAghhBDiR4ERIYQQQogfBUaEEEIIIX4UGBFCCCGE+FFgRAghhBDiR4ERIYQQQogfBUaEEEIIIX4UGBFCCCGE+FFgRAghhBDiR4ERIYQQQogfBUaEEEIIIX4UGBFCCCGE+FFg1AV88sknOPfcczFq1ChMnz4db7/9dmcXqdO4XC4sWbIEV155Zdg2LfXUW+qyqakJ999/P0pKSjBmzBicd955eO+99xT7UH0Bu3fvxvz583H88cdj3LhxuOSSS7BhwwbFPlRP4Xiex+mnn46hQ4cqXqe6Cjp06BCGDh0a9v/XX38t7UP1Fe7dd9/FzJkzMWrUKEyaNAnPPvustK3L1JdIOtX69evFYcOGiXfccYf4ySefiDfffLNYVFQkrlu3rrOL1qFqamrE559/XpwyZYpYVFQk/uEPf1Bs11JPvakub7vtNvHCCy8U//e//4kffvihuGDBArGoqEh87733RFGk+gr43//+J959993imjVrxHfeeUe88MILxZEjR4o7d+4URZHqKZI33nhDLCoqEouKiqTXqK6UtmzZIhYVFYlr1qwRv/rqK+n/mpoaURSpvtQsWbJEHDlypPjQQw+Jn376qfjOO++Iq1evFkWxa9UXBUad7Pzzz1cEATzPi2effbZ46aWXdmKpOt6KFSvEY445Rly2bJk4e/bssMBISz31prr85ptvREEQpOeCIIjnnHOO9P6pvtSVlZWJRUVF4ooVK0RRpHpS09DQIJ5wwgniOeecowiMqK6UvvjiC3Ho0KGi2+1W3U71pbRt2zZx6NCh4tq1a1W3d6X6oqa0TlRZWYnt27fj3HPPlV5jWRYzZszAli1b4HA4OrF0HWvGjBn45ptvsGDBAnAcp9impZ56W12ecMIJYBhGes4wDAoKClBXV0f1FYVOpwMApKenUz1F8Oijj6KwsBCnnXaa9BrVVbi6ujpYrVZ4PB4IgqDYRvUVbuXKlRgzZgymT58OURTB87y0ravVFwVGnWjPnj0AgKKiIsXrgwYNgiAIOHz4cGcUq1P069cPJpNJdZuWeurtden1erFt2zYUFRVRfYVwuVyoq6vDli1bcNttt6G4uBinn3461ZOK77//HqtWrcKdd96peJ3qKlxdXR0aGxsxbtw4jBw5EhdddBG+/fZbAFRfajZs2IAJEybgvvvuw9ixYzFq1Chce+21qKqq6nL1RYFRJ6qrqwMApKSkKF632WwAgObm5g4vU1ekpZ56c12Kooj77rsP1dXVuOqqq6i+Qtx444047rjjcMkll4DneSxfvhwGg4HqKURzczMWLVqEq6++OuzmQ3UV7sQTT8Rzzz2Hl156CXfddRcaGhpwzTXX4Mcff6T6CtHc3Iyqqip8/PHHqK+vx9KlS3Hbbbdh8+bNuPXWW7tcfekSdiYSN6/XC8CXDpQLNJHIm0p6My311Fvrsrm5GbfeeivWr1+Phx56CKNHj8b+/fsBUH0F3HrrrbjqqqtQWlqKN998EzNnzsRLL71En6sQ//d//4eUlBRcf/31YduorsINHjwYgwcPBgBMnjwZp512Gs4880y8+OKLmD59OgCqr4BA0NKnTx88+uijAICTTjoJAPDQQw9h4sSJALpOfVFg1IksFguA8Ei3qakJAJCamtrRReqStNRTTU1NzH16mt27d+P666+H1+vFK6+8gjFjxgCg+golv4Gde+65OPvss7FkyRJceOGFAKieAOCFF17AunXr8Nprr8Hj8Uj/A0BLSwt9pjTIzMzE+PHjsXfvXqqvEAaDAYCvb6TccccdB8DXlQL4//buPCir6n/g+FsEF5RwoVJJNElEtpRwIxI1S9TUYMQcsaTSUcBwRx2QcolFHU3HZSBxA3KQ0vRBMDUHnSQ3FNcEhRwVCzAERIWH5fz+AJ6fj2BKo1/4fvu8Zu4M99xzztz7mWeYz5x77jmNJ17yKq0Bvf766wBkZWXplWdlZdGiRQs6d+7cELfV6DxLnP5tsczKysLLy4tu3bqxZ88eXVIEEq+/07x5c+zs7Lh+/brE6RGxsbGUlJTg7u6Oo6Mjjo6OuvVlHB0dSUhIACRWT1NWVkbz5s3lt/WYtm3bYmxsTFFRkV55zShQbm4u0HjiJYlRA7K0tKRTp066fzoAlZWVJCUl4erqqvuC5t/uWeL0b4vlV199Rc+ePVm/fj0mJiZ61yRe/+/+/ft656WlpVy4cIGuXbtKnB6xZs0aYmNj9Q4PDw+gKmmaOXOmxOoxNSNqNW7evElqaip9+vSR39ZjmjRpgrOzMwcOHECr1erKk5OTMTQ0xN3dvXHF67l+/C/qbefOncrKykotWbJEHTx4UM2aNUu9+eabKiMjo6FvrcFMnDix1jpGzxKnf0ssi4uLlbW1tVq0aJHewnI1R1FRkcSr2ujRo9XKlSvVvn371Pfff6/GjRun7Ozs1Llz55RS8rv6O2vXrtVbx0hipW/OnDkqLCxMJSYmqq1btyoXFxfl7Oys/vzzT6WUxOtxFy9eVLa2tmrixIlKo9Gob775RtnY2KiQkBClVOOKlyRGjcC2bdvUoEGDlJ2dnfL09FSnTp1q6FtqUHUlRko9W5z+DbGsWaTwScfZs2eVUhIvpZSKiIhQ77//vrK3t1cuLi7Kz8+v1j9RiVPdHk+MlJJYPSo6OloNHjxY2draqr59+6pZs2apmzdv6tWReOlLSUlRHh4eytbWVg0cOFCtX79eVVRU6K43lng1UUqp5zf+JIQQQgjx30vmGAkhhBBCVJPESAghhBCimiRGQgghhBDVJDESQgghhKgmiZEQQgghRDVJjIQQQgghqkliJIQQQghRTRIjIcT/jJq9l+p7TQghakhiJIRoUA8fPmTbtm1UVlbqlYeEhLB+/fonttu9ezfTpk3TK1u0aBEBAQF11p8+fTq+vr7PdE/r169Ho9E88fr27dtZtWoVFRUVT6xT8zxpaWksXLhQV/7w4UPd38ePH2fatGm1dgwXQjQcSYyEEA3q5MmThIaGEhQUxKML8WdmZnLz5k3deXBwMB9//LHuPC8vj4yMDN15bm4uGo2GAQMGAFBRUUF5eTnl5eUUFxfz66+/0rt3b11ZzfF4cqPVatm0aVOtncBrXLp0iZCQEKKiorh27VqddRYsWMDy5csByM7O1m18mZ+fz4gRI9i7dy9QlRhlZGTQunXrZ46XEOLF+t/ZvlcI8V/J1dWVgIAAwsPDadWqFYGBgf+on8jISLp06cLo0aMB8Pb25uTJk3p1Vq5cycqVK/XK2rRpw4kTJ8jPz+fu3btcuHCBBw8e0L17dzIzMwEwMjLCwsKCiooKlixZwoABAygpKWHRokV89913tXb2Hj58OFOnTsXBwUFXVlFRwezZszE1NWXo0KEAnD59Gmdn53/0vEKIF0MSIyFEg/vss8+4evUqqampFBcX13sEJTs7m7i4ONauXUvTpk2Jiopi8uTJLFu2DIDAwEAMDAxYunRprbYGBlUD55s3b+bbb7/VlT86OmVubs7hw4dZt24dGRkZ7N27l9LSUtzd3Vm8eHGtfl1dXVmwYAHW1tb89ttvAJSWltKhQwdCQkIwNjYmPz+fM2fOYG5uTnx8vF77jh074uLiUq8YCCGeD0mMhBCNwuLFi6moqKBly5b1bhseHo6TkxODBw/m/PnzLF++nLCwMFxdXSkoKCAtLY2vv/6aLl26PLGPuXPnMnfuXIYMGcKUKVO4ceMGI0aMwN7eHqia07Rx40bCw8Pp3LkzUPV6LygoCENDQ4KCgmjatCkpKSnExMQAVa8Jc3JyKC8vZ+7cuQAsW7YMb29vLl++jKGhIVevXuXq1atA1etDY2NjRo0aJYmREA1EEiMhRIPJysri/v37AJiYmNC1a9d693Ho0CEOHz7Mnj17qKioYMWKFbz99ttYWVkRHBzM7du3KSsr49ixY6SmpgJVr7pq5iI96tKlS/zxxx+89957jBw5Ejs7O+zt7YmPj+fLL7/E29ubMWPG6Op7enpy8+ZNIiIiyMjIIDg4mHbt2tGrVy8ACgsLOXr0KAYGBroygJdeeont27fj4OCgS6IAXFxc+Oijj/jiiy/qHQchxPMhiZEQosEEBgZy5swZoCopiIqKqncfR44coby8nFGjRulGnDQaDffu3QPgxIkT2Nvb06JFCwA0Gg3dunWrMzE6cOAAffr0wczMTK//oKAgPDw8mD9/fq02s2fPxsTEhNWrV5OcnMzUqVOxtrYmMzMTX19fXnnlFXJyckhMTMTf358hQ4awdetWsrOz9frRarX89ddfvPbaa/WOgRDi+ZHESAjRYCIjIykrK2PZsmUUFhbWul4z/+fv+Pj46EZxfH198ff3173qGjZsGHFxcYSEhGBlZQXAL7/88sS+9u/fz6RJk/TKBg4cSHh4OO+88w7vvvsuq1at0hv9uXPnDlOmTMHZ2Rlra2sqKyuJjY1l9erVjB8/HhsbGxYuXMi4cePw9/fXTQrv3bs3aWlpFBQU0KZNG65cuUJlZSWWlpZPfWYhxIsjn+sLIRqMiYkJ7dq1o1mzZrWuabXaWl971aVTp044OTmxY8cOHBwc8PLy0l3buHEjLi4uuqTo71y5coXr16/zxhtvkJmZSWVlJbm5uWRlZeHi4kJlZSXZ2dmUlpbq2sTExDBo0CAOHz6Mra0tTZs2JTw8nIiICMLDwwkICKBJkyYATJgwgaioKNLS0liyZAkbNmzA0NCQ06dPA3DmzBlatmxJz549n3qvQogXR0aMhBCNUklJyTNPxI6Li+PYsWNoNBru3r1LVlYWXbp04dSpUwD06NFDr35oaCihoaG68/T0dK5fvw7of40WFhZGWFgYXl5e+Pj46MqLi4sJCQlh165dzJgxgyFDhuiu+fj44Ofnx0svvcTq1au5cOECaWlpAPTr149+/frp6jo4OJCUlMTQoUM5ePAgTk5OGBkZPVuAhBAvhCRGQohGqbCwEFNT06fWu3z5MkuXLqV169Z88MEHFBQUAJCYmEhiYmKt+t7e3ri7u+tNogZwc3MjPT1dd96vXz+Cg4MZOXIkULWgJMDRo0cJCAhAq9USGRnJwIED9fpZtWoVcXFxemU2Nja6v2s+/QcYMWIE4eHhHD16lNTUVMLCwp76vEKIF0tepQkhGg2tVkt0dDSVlZXcvn2bjh07PrWNgYEBFhYW9O/fn08++YR169aRmJhIt27dsLS0RCnF5s2bMTMzw9LSEiMjI9q1a8e9e/fYsmVLvef0bNq0CScnJ/bt21crKQLw9/cnISEBCwsL3NzcdAlaYmIivXv31kuSxo4di4mJCdOnT8fMzIxhw4bV616EEM+fjBgJIRqFkpISfHx8KCwspH///pSVldGtW7entrO2tq5zZKjGhg0buHjxIq1atdIrr6ioID4+HhcXF9zc3CgvLycvL4+cnBxycnLQarXs2rWLhIQETExMmDdvHlC1ZtKHH36o19fOnTu5ffs2M2fOxMzMjJ9++okbN24wfvx4LCwsMDIyQqPRcOHCBXbv3q1r16JFC9zc3IiNjcXT0/MfreEkhHi+JDESQjS4goICTp8+jbm5OVFRUfz8888YGxvXeyJyzWrSZ86c0SUvSUlJrFixotZE7rfeeosxY8awfPlyBg0axPz589m/fz9QtQVIeXk5ubm52Nra4uTkpGtX1yjW77//ztmzZ3Xnnp6eAERERLB9+3aGDx9OTEwMgYGBehPBjx8/Tnx8PAYGBsTExODm5lZrPpQQ4j9LXqUJIRpUQUEBKSkpdO/enbi4ODp06EB0dDRubm51fq32uKSkJGbNmsXQoUMZMGAAfn5+/Pjjj1RWVrJ48WJ69uypmycEVbve13wpNnv2bO7cuUNkZCSTJ08mNjaWI0eOcP78eUxNTZk2bRphYWGMHTtW1/7RjW5r3Lp1C3Nzc915s2bN8PLyIjo6mqZNm7JlyxYAcnJyuHPnDgBnz57Fz88PS0tLfvjhB5o3b87nn3/OuXPn/lkghRDPhYwYCSEaVJs2bZg+fTpjx46lbdu2zJs3j6KiInx9fZ+pfWZmJteuXcPZ2RlHR0d69+5Nly5dSExMJDU1lR07duj2K8vLyyM3N5eXX34ZgA4dOuDl5UV2drZu648nMTU1xdDQkH379tG+fXvdGks3btzg2LFj+Pv7A/DgwQNSUlI4dOgQCQkJdO7cmcjISNLT04mMjKSkpAQLCwtCQ0OxsLAgKiqK9u3bs2nTJiZNmsSECRPw8/N75ucXQjxnSgghGonk5GRla2urNBpNrWv5+fkqNzdXdx4REaEGDx78xL60Wq1KTk5WSim1YMECZWVlpWxsbNSnn36qSkpKdPXKysrqbN+3b1+VkJCgV7ZlyxbVt29fZWVlpTt69eqlZsyYoR4+fKiUUmrOnDmqR48easKECUqj0ajy8nK9Zzh37pyytbVVvr6+qqioSK//W7duKQ8PD7VmzZonPpcQ4sVqolQd48JCCNFA8vLydCM6z1PNK7Sa12gvSm5uLkopXn311SfWSU9Px8rKqs570Wq1NGnSRNYzEqKBSGIkhBBCCFFNJl8LIYQQQlSTxEgIIYQQopokRkIIIYQQ1SQxEkIIIYSoJomREEIIIUQ1SYyEEEIIIapJYiSEEEIIUU0SIyGEEEKIapIYCSGEEEJU+z+cVOOoA8Q7pAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rl_utils\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "\n",
    "rl_utils.picture_return(return_list, 'DDPG', 'GridWorld')"
   ]
  },
  {
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   "source": [
    "# 重要发现\n",
    "\n",
    "对于DDPG算法中的take_action函数\n",
    "```python\n",
    "def take_action(self, state):\n",
    "    state = torch.tensor(state[np.newaxis, :], dtype=torch.float).to(self.device)\n",
    "    action = self.actor(state).detach().cpu().numpy()[0]  # * 动作是数组, 用.detach()把action分离出计算图\n",
    "    # 给动作添加噪声，增加探索\n",
    "    action = action + self.sigma * np.random.randn(self.action_dim)\n",
    "    return action\n",
    "```\n",
    "\n",
    "\n",
    "第二行中，`state`需要是二维才能传入网络中计算，原本是一维，所以要多加一个维度，即`array([list])`变成`[[list]]`或`array([[list]])`，在原本的[教学代码](https://hrl.boyuai.com/chapter/3/%E7%9B%AE%E6%A0%87%E5%AF%BC%E5%90%91%E7%9A%84%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0)中，写为`[state]`，虽然效果一致，但是会告警：建议先转成`ndarray`。\n",
    "\n",
    "因此在前面一些代码中，如PPO中，我写的是`np.array(state)`，其实这是错误的，因为没有加上新维度，但一直没有出现太大问题，但是在本节代码中发现，写为`np.array(state)`导致收敛效果很差，但是如果写作`state[np.newaxis, :]`，既不会告警，又能保证收敛效果好，***因此决定修改所有相关部分***。"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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